Brain-derived Signals Related to Ball Kicking Movement in Soccer and Technologies Employed: A Systematic Literature Review With Gap Map

Brain-derived Signals Related to Ball Kicking Movement in Soccer and Technologies Employed: A Systematic Literature Review With Gap Map | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Brain-derived Signals Related to Ball Kicking Movement in Soccer and Technologies Employed: A Systematic Literature Review With Gap Map Nicole Unsihuay, Filipe Manuel Clemente, Robert Trybulski, Rohit Kumar Thapa, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8940911/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Apr, 2026 Read the published version in BMC Sports Science, Medicine and Rehabilitation → Version 1 posted 15 You are reading this latest preprint version Abstract Background Recent technological advances have enabled the development of portable data acquisition systems that facilitate the collection of brain signals during sports tasks. The main objective of the present systematic review was to collate evidence regarding studies that have analysed brain-derived indices related to ball kicking action. Methods The PRISMA guidelines were followed in the previously established protocol for this review. Six electronic databases were used for searches (IEEE Xplore, Scopus, Web of Science, APA PsycNet, EBSCOHost, and PubMed). The search string was formulated based on the following PICOS/PECOS framework: participants as human able-bodied subjects regardless of age, evaluated while performing a ball kick task, and reported results of brain-derived metrics. The STROBE checklist was used to evaluate the methodological quality of the included studies. Results The database searches resulted in a total of 1748 records, of which 8 original research articles met all the inclusion criteria. Most studies used EEG systems while few employed fNIRS. Qualitative synthesis indicated that skilled ball kicking performance was accompanied by phase-specific cortical dynamics (e.g. within frontal, sensorimotor/central, and parieto-occipital regions). Conclusions Better outcomes tended to be linked with brain patterns related to efficient attentional allocation and visuospatial processing, whereas anxiety and injury appear to shift cortical engagement toward potentially compensatory, less efficient control strategies. Finally, one problem identified in this review was that only 25% literature studies used an opponent attempting to block the shots. Future studies need to improve the design of experimental tasks so that they more closely resemble what occurs in a real game. Trial registration The review protocol was registered in OSF Preregistration under ID #NZASB. measurement portable neuroimaging systems EEG fNIRS team sports biomechanics Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Ball kicking has been a determinant variable of match performance (i.e. outcomes) in male and female youth or senior soccer, given the strong influence of shooting on target upon winning results [ 1 – 6 ], motivating extensive scientific analysis of the movement [ 7 ]. Indeed, the kicking action consists in the most widely studied skill across the biomechanics of soccer literature, reflecting its technical importance and recurrent use across playing situations [ 8 ]. Ball kicking is characterized as a multiarticular ballistic action, involving proximal-to-distal motion sequencing of the lower-limb segments that may directly influences on the development of foot velocity and ball placement-derived outcomes [ 9 ]. Throughout the history of biomechanical - and motor control - analysis of kicking in soccer, the methodological progress from 2D (two-dimensional) kinematics toward 3D (three-dimensional) full-body biomechanics has improved descriptive and explanatory models of kicking, while simultaneously emphasizing the need to capture movement coordination across the whole body and task phases [ 10 ]. Deterministic models [ 11 ] have indeed been previously tested regarding the mechanical factors related to ball kicking outcomes in both youth and senior players [ 12 , 13 ]. Nevertheless, even though there is a mature knowledge base on the characterization of biomechanical features and the effects of individual and environmental constraints upon soccer ball kicking [ 7 , 9 , 10 , 14 , 15 ], understanding of brain–behavior relationships in ecologically valid sports actions remains comparatively limited, particularly for high-speed skills executed under accuracy and pressure constraints [ 16 ]. Skilled motor performance depends on distributed central cortical networks that support voluntary movement control and exhibit experience-dependent plasticity [ 17 ]. In general, motor skill acquisition occurs through time-dependent stages that include rapid within-session gains and later consolidation processes that stabilize performance [ 18 ]. Furthermore, accurate skilled actions further rely on adaptive internal models that use sensory prediction errors to attempt refine motor commands and maintain calibration [ 19 ]. In electrophysiological terms, self-initiated actions are preceded by the Bereitschaftspotential (i.e. early cortical activation preceding self-initiated movements), reflecting preparatory activity across medial and lateral motor areas with temporospatial structure related to movement generation [ 20 ]. Complementarily, event-related desynchronization/synchronization in mu (standard alpha band) and beta rhythms provides time-resolved indices of sensorimotor activation and inhibition during preparation, execution, and imagery [ 21 ]. Lower-limb movements also exhibit somatotopically organized oscillatory dynamics over sensorimotor regions, underscoring the feasibility of tracking leg-related cortical processes using scalp electroencephalogram (EEG) [ 22 ]. Since many neuroimaging paradigms constrain natural movement, mobile brain/body imaging concepts have been proposed to link distributed brain dynamics to real-world action while recording behavior with high bandwidth [ 23 ]. Mobile brain imaging specifically enables simultaneous recording of EEG with body dynamics during active behavior, and this may support study designs with improved ecological validity [ 24 ]. Advances in lightweight systems and synchronized multimodal acquisition have further operationalized “natural cognition in action” approaches that integrate EEG with motion and physiological measures [ 24 ]. However, movement-induced artifacts and other non-neural contamination typically increase with movement intensity, and methodological consensus on optimal mitigation strategies remains incomplete [ 25 ]. Foundational demonstrations show that artifact attenuation (e.g., template regression coupled with independent component analysis (ICA)) can recover meaningful EEG features during whole-body locomotion, supporting the broader feasibility of mobile EEG in dynamic tasks [ 26 ]. At the same time, semi-periodic movement artifacts can sometimes produce components that resemble plausible brain sources, requiring careful validation when interpreting source-resolved results [ 27 ]. As concerning soccer-specific kicking studies, recent portable EEG experiments have begun to evaluate/explore the potential link between brain-derived signals and performance outcomes [ 28 – 30 ]. Despite this growth, mobile EEG in movement contexts still lacks broad consensus for quantifying and characterizing artifact burden [ 31 ]. In parallel movement domains, systematic evidence on EEG–EMG connectivity shows substantial methodological variability and explicitly calls for better alignment of approaches, reinforcing the likelihood of similar harmonization needs in kicking research [ 32 ]. Alongside EEG, portable hemodynamic imaging such as near-infrared spectroscopy has long been positioned as a practical approach for studying cortical involvement during exercise and motor tasks in moving humans [ 33 ]. In movement science, functional near-infrared spectroscopy (fNIRS) applications have also been expanded, despite with heterogeneous protocols and data-processing choices which may limit comparability and interpretability across studies [ 34 ]. Consensus-oriented recommendations for fNIRS in posture and gait explicitly emphasize standardized conduct, artifact handling, and reporting, and such issues have been directly relevant when translating neuroimaging to complex sport skills [ 35 ]. These developments indicate a timely need to consolidate what is known about brain-derived signals during soccer kicking and the technologies used to measure them, to better support performance science and rehabilitation translation [ 36 ]. With these assumptions in mind, the current literature review aims to (i) systematically collate and critically evaluate the evidence on brain-derived signals reported in original research studies in relation to ball-kicking action in soccer and (ii) map the acquisition technologies and analytical pipelines used to capture and process these signals. We additionally aim to identify current methodological and translational gaps and derive evidence-informed directions for future neuro-mechanical research on soccer kicking. MATERIALS AND METHODS The protocol for the present review followed the items [Additional file 1] suggested by The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement [ 37 ] and was registered prior to the execution of the methodology processes (2025-06-09 04:03 PM GMT − 5) in the OSF – Open Science Framework under ID #nzasb (DOI: 10.17605/OSF.IO/NZASB ), considering the structure according to a recently published systematic review [ 38 ]. All the papers included in the current systematic review (i.e., qualitative synthesis of evidence) reported ethical aspects adopted for data collection in its full-texts and this was defined in as an inclusion criterion in the aforementioned protocol. Search strategy The electronic databases IEEE Xplore, Scopus, Web of Science, APA PsycNet®, EBSCOHost, and PubMed were searched on 12/06/2025 (between 03:21 to 07:56 PM GMT − 5) in an attempt to identify evidence about brain-derived signals related to ball kicking movement in soccer, published as articles within scientific journals. The search strategy and key terms were defined according to previous systematic reviews that analyzed on a separate basis the brain activity or ball kicking in the soccer context [ 14 , 39 , 40 ]. Using a Boolean search strategy, the final search string consisted of (soccer OR football* OR association football OR 11-a-side) AND (kick* OR shoot* OR pass* OR ball handling OR ball-kicking OR goal-directed OR skill OR technical) AND (brain OR cortex OR cortical OR neural OR neuronal OR EEG* OR electroencephalography OR fNIRS OR nirs OR functional near-infra*). The searches focused on the fields of title, abstract and keywords across all the aforementioned databases. Eligibility criteria This review only considered original studies that were (i) scientific articles peer-reviewed; (ii) with abstract available for screening in the respective electronic database; (iii) full-text published in English language; using a PICOS/PECOS framework [ 41 ]: (iv) Participants – when included human able-bodied subjects (e.g. soccer players) regardless of age; (v) Intervention/Exposure – ball kick task; (vi) Comparator – not applicable; (vii) Outcomes – reported results of brain derived metrics (e.g. EEG power) and (viii) Study design – no restrictions were imposed. No restrictions were also outlined in reference to the date of publication (i.e., articles were searched from inception up to the date of search completion reported here). Study selection process Initially, the search results from all the databases were imported into an open-source reference management software (Zotero v.7.0.15; Corporation for Digital Scholarship, USA), which was used for the execution of this whole protocol. Thereafter the duplicates were removed automatically using its “Duplicate Items” function. Following on two authors (N.V. and F.Z.) independently evaluated the title, abstract and keywords of all registries identified in the electronic database search. Discrepancies were resolved in an additional consensus round with a third author, that consists also in a senior researcher in the area (L.V.). The evaluators were instructed to consider the eligibility criteria mentioned above and also to not consider inclusion of studies (i) published as book chapters, conference proceedings, or similar types (i.e. grey literature); (ii) if the article was later retracted; (iii) experiments conducted without reporting of any ethical aspects in the article text or the eventual exoneration – if applicable [ 42 , 43 ]; (iv) considered task movements pertaining to football codes distinct to soccer; (v) with no information concerning the method/equipment adopted to record brain activity or (vi) with no information about which specific brain regions were considered for data collection. Data extraction and evidence synthesis The following information was extracted from included studies: publication details – authors, year of publication and funding; demographic characteristics – geographical location of the study, number of participants, sex, age, playing level and positional role; task protocol – location, instruction/aim, ball characteristics, limb(s) used; data outcomes – cortical areas evaluated, data collection device and acquisition frequency; signal processing techniques and pertinent findings. These items were selected taking into account previous systematic reviews [ 14 , 40 , 44 ]. One author conducted the data extraction (M.F.) and a second author verified the extracted data (N.V.). In addition, to critically appraise the evidence collated, the STROBE checklist [ 45 ] was used to extract and provide a qualitative analysis (e.g. methods) for each of the of included studies. In particular, the Items 1, 3, 6, 8, 12, 14, 18, 19, 20 and 22 were selected for the present review. Two authors independently evaluated each study individually (N.V. and F.L.C.) and a third author (L.V.) checked the ratings and resolved any discrepancies if existed. Each item was rated using a numerical scale ("completed" = 1 or "incomplete" = 0). A sum of all items was then computed for each study. Based on that, the included studies were deemed to have low (∑ ≥ 8) or high (∑ ≤ 7) risk of bias [ 46 , 47 ]. The robvis tool was used to generate the corresponding plot of methodological quality outcomes [ 48 ]. Finally, gap maps were also constructed to assist provide a synthesis of the findings across studies as well as potential directives for future investigations [ 49 ]. The threshold for judgement of a given study result as statistically significant was set at p ≤ 0.05 unless otherwise stated. RESULTS A total of 1748 reference entries were initially identified when pooling all results of the searches in the databases (IEEE Xplore – n = 148, Scopus – n = 573, Web of Science – n = 451, APA PsycNet® – n = 1, EBSCOHost – n = 408, and PubMed – n = 167). Next, 783 records were removed before screening due to automatic identification by the reference manager software as being duplicates (n = 759), published in a non-English language (n = 24) or retracted works (n = 4). Regarding the duplicates, the items deemed as the same indeed were merged and this process was supervised case-by-case. In the next stage, 236 reference entries were deleted owing to the fact that they were classified as grey literature, resulting in 729 records sought for retrieval. Following inspection of the titles, abstracts and keywords, 18 reports were assessed for eligibility, i.e. the step of full text analysis. After reading their full-texts, 8 [28–30, 50–54] were finally included in the review (Fig. 1). In the last screening stage, exclusions were due to (i) the use of a only virtual soccer task, (ii) only resting state measures reported, (iii) no brain outcomes reported, (iv) the use of only motor imagery paradigm, (v) paper published as conference proceeding, (vi) opinion piece article and (vii) covering other football codes. ****Insert Fig. 1 here**** Main sample characteristics and purposes of studies Table 1 presents the sample characteristics and purpose of the included studies. Of the eight studies (with 183 participants), six included seniors, and one included youth participants, while one study [50] did not provide information on the age of the participants. Four studies (50%) involved only male participants [28, 29, 50, 53], three (38%) included both male and female participants [30, 51, 52], and in one study this information was not reported [54]. All studies included semi-professional or non-expert players; none included professional or elite players. Two studies reported the inclusion of samples from all playing positions [28, 29], while in the remaining studies (75%), the participants' positional roles were uncertain or unspecified. Sample sizes varied from 10 participants [29] to 39 participants [53]. One of the early studies, conducted by Collins et al. [50] in 1991, investigated changes in cerebral activity (i.e., alpha frequency) during a ball-kicking task among non-soccer-playing, physically active male participants. The participants were instructed to kick a soccer ball from a distance of 7 meters through a 30-centimeter-wide channel marked by two cones, while their cerebral activity was being recorded. Table 1 Sample characteristics and purposes of the included studies. Reference Year Location Study purpose N Gender Age Playing level Playing position(s) Collins et al. [50] 1991 UK To investigate whether the changes in the alpha frequency band observed in experts during the karate modality also occurred in individuals relatively novice to the kicking task. 22 Male -- Sub-elite unspecified Li et al. [29] 2025 Germany To investigate whether the changes in the 8–13 Hz frequency band are involved in successful and unsuccessful penalty kicks in skilled soccer players. 10 Male Senior Sub-elite All (pooled) Palucci Vieira et al. [28] 2022 Brazil Identify the magnitude of possible associations between the electroencephalographic signals and lower limb kinematic parameters during ball kicking action 24 Male Youth Sub-elite All (pooled) Piskin et al. [51] 2024 Germany To investigate the cortical dynamics involved in target-directed kicking based on source-based analysis. 11 3 female/8 male Senior Sub-elite unspecified Piskin et al. [52] 2025 Germany To compare the kicking performance and associated cortical activity between injured and healthy soccer players. 25 10 female/ 15 male Senior Sub-elite unspecified Piskin et al. [30] 2024 Germany To compare the neural mechanisms involved in passing between novice and experienced players. 30 13 female/17 male Senior Sub-elite Unspecified Schmaderer et al. [53] 2023 Germany To investigate the prefrontal activity of soccer experts during general and sport-specific cognitive tasks. 39 Male Senior Sub-elite Unspecified Slutter et al. [54] 2021 Netherlands To compare brain activity during a penalty kick in which soccer players felt anxious and not anxious, using fNIRS. 22 -- Senior Sub-elite No Palucci et al. [28] in 2024 studied the association between brain oscillations at different frequency bands (i.e., delta, theta, alpha, beta, gamma) across different regions of the brain (i.e., frontal, motor, parietal, and occipital) during three phases of soccer kicking (i.e., preparation phase, approach phase, and immediately before ball contact phase) and ball velocity and mean radial error. The authors [28] involved 24 male U17 soccer players competing in regional-level competitions. In another study, Li et al. [29] in 2025 investigated EEG power in the 8–13 Hz frequency band at frontal and central regions before penalty kicks, comparing successful and unsuccessful attempts. The study [28] involved 10 right-footed male skilled soccer players who participated in regional-level tournaments in Germany. Piskin et al. [51] in 2024 investigated cortical dynamics (measured using EEG) during soccer kicking, specifically aiming to pass the ball towards a target located three meters away. In another two studies, one also in 2024 and another in 2025, the authors used similar methodological procedures [27] and compared the cortical dynamics during soccer kicking between experienced and novice participants (Piskin et al. [30]) as well as between participants with an injury (i.e., anterior cruciate ligament-reconstructed players) and healthy individuals (Piskin et al. [52]). Schmaderer et al. [53] in 2023 investigated the cortical processing of experienced soccer players during general and sport-specific tasks. The participants were involved in a sport-specific task requiring them to pass the ball against one of the three back-pass walls. A green square was the signal to pass amongst the three target walls. For the general cognition test, the participants were asked to stand in front of a 2 x 3 m wall and choose the accurate visual stimuli. A total of eight visual stimuli generators were present, and one color would change, and the participants had to move their hands in front of the stimulus. Slutter et al. [54] in 2021 compared brain activity during penalty kicks with a friendly goalkeeper, an amiable goalkeeper, and a competitive goalkeeper. In addition, the authors [54] compared the brain activity during these penalty kick conditions between experienced and inexperienced participants. The authors [54] also investigated the association between anxiety and brain activity during these penalty kick conditions. ****Insert Table 1 here**** Overview of data collection methods and technologies used Table 2 presents the data collection procedures and technologies used in the included studies. In this sense, two neuroimaging modalities were identified across studies: electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). The earliest study by Collins et al. [50] did not specify the brand of the EEG used to collect the data. Three studies [30, 51, 52] used EEG system from antiCap (Brain Products, Germany) while two studies used ANT Neuro (ANT Neuro b.v., Netherlands; Germany) [28, 29]. Regarding fNIRS, one study each utilized NIRx technologies (NIRx Medical Technologie, USA) [53] and Artinis Brite (Artinis Medical Systems, The Netherlands) [54]. The acquisition frequencies for EEG studies ranged from 250 Hz [50] to 1024 Hz [28] and most studies (4 out of 8 studies) used 500 Hz. The acquisition frequencies for fNIRS studies ranged from 8.7 Hz [53] to 10 Hz [54]. The experimental protocol used in the included studies varied, with only three studies [30, 51, 52] using similar protocols. Regarding the number of trials, these ranged from 6 trials [50] to 90 trials [30, 51, 52], with most studies using between 15 and 40 trials [28, 29, 53, 54]. Most studies evaluated the preferred limb, and only one study evaluated both the preferred and non-preferred limbs [50], while another study did not specify this methodological aspect [53]. The testing was conducted in laboratory conditions in three studies [30, 52, 53], and in outdoor conditions in two studies (artificial soccer pitch [54], and on natural grass pitch [28]), while three studies did not specify the environmental conditions [29, 50, 51]. Common signal processing procedures in EEG studies included re-referencing/down-sampling, passband filtering, artifact suppression (by visual inspection and/or automatically with pre-defined thresholds), rejection of bad channels and rejection of components by independent component analysis (ICA). From the six studies assessing EEG, five [28–30, 51, 52] processed the data in the EEGLAB environment [55]. In this sense, Palucci Vieira et al. [28] used EEGLAB v2020.0 while Li et al. [29] do not specified the version of the toolbox used. The three studies by Piskin et al. [30, 51, 52] used the EEGLAB version 14.1.2b with identical processing methods across studies. As regarding fNIRS studies, processing procedures consisted generally in, as for example, passband filtering, automatic removal of data based on pre-defined thresholds and data transformation. Full details of the procedures for each study can be found in the Table 3. Schmaderer et al. [53] used the nirsLab software package v.2019.4, as did Slutter et al. [54] used OxySoft software. Complementary measurements generally consisted in motion kinematics: three studies [30, 51, 52] used wearable IMU systems (myoMOTION, Noraxon), one study used 3D videogrammetry [28] whilst another 2D [54]; both works employed GoPro® cameras for video recording. Table 3 Brain-derived data analysis and associated main outcomes in the included studies. Reference Brain channels collected Frequency bands Signal processing (software and procedures) Phase(s) of the movement analyzed Brain-derived outcomes computed Additional variables Summary Collins et al. [50] T3, T2, C3, C4, P3, P4; Reference: Cz; Ground: spine (Thoracic 2). alpha: 8–13 Hz CEAN 400 Acquisition System; Digitized amplified signal at a sampling rate of 256 Hz; Filters from 1 to 100 Hz; Frequency analysis by Fast Fourier Transform (FFT) in a spectrum from 1 to 60 Hz, using a cosine bell window with 256 points; alpha frequency band (8–13 Hz) Preparatory (-2s) EEG-derived average power spectral density Successful and unsuccessful performance of kick The authors identified that greater activity in the alpha frequency band before a kick can predict successful kicking performance in non-expert individuals Li et al. [29] Fz and Cz; Reference: M1 and M2; Ground: AFz alpha: 8–13 Hz EEGLAB: re-referenced to the averaged (m1, M2); bandpass (1–30 Hz); extracted epochs (-3000-1000ms); removed bad channels; rejected artifacts above ± 100 µV; ICA; interpolated channels Preparatory (-2s) EEG- derived power through Welch estimation method Learning effect: compare the success rate across the three blocks; Anxiety level: compare between and within subjects during the football penalty task Success in penalty kicks in a difficult task is characterized by lower power levels of 8 to 13 Hz in the frontal and central regions, indicating effective neuromotor allocation without compromising performance Palucci Vieira et al. [28] F3, Fz, F4; C3, Cz, C4; P3, Pz, P4; O1, Oz, O2 Reference: Common average Ground: -- delta: 0.5–3 Hz theta: 4–7 Hz alpha: 8–12 Hz beta: 13–30 Hz gamma: 31–50 Hz EEGLAB v2020.0: Butterworth band-pass (0.3–50 Hz); notch filter (60 Hz); data re-sampled to 512 Hz; artifacts rejected by 1) visual inspection 2) channels with kurtosis > 3 standard deviations from the mean value, 3) epochs with absolute difference > 150 mV and 4) ICA Preparatory (-6 to -3 s), approach (-3 to -1 s) and impact phase (-1 s to ball impact) EEG-derived average power spectral density; ERS/ERD; ERSP and ITC 3D Kinematics: Angular joint (hip, knee and ankle) displacement and velocity; ROM; foot velocity; ball velocity; 2D kinematics: kicking accuracy Authors claim the association between kicking ball velocity and EEG-derived frontal theta power while kicking accuracy was related to occipital alpha power Piskin et al. [51] Clusters of brain sources, whose locations occurred in the parieto-occipital and mid-frontal regions; Reference: FCz; Ground: AFz theta (4–7 Hz); Alpha (8–10 Hz); Alpha-2 (11–13 Hz); Beta-1 (14-20Hz) EEGLAB toolbox (version 14.1.2b): remove sinusoidal line noise; band-pass (3 -30Hz); artifacts rejected by 1) remove robust z-socre > 5; 2) Missing channels were interpolated; 3) data were then re- referenced to a common average and downsampled to 256 Hz; 4) an epoch time window of 3000 ms before and after kick-onset; 5) Baseline correction was performed from − 2500 to − 2000 ms; 6) ICA Preparatory (-2s); Banckswing and swing: 0–1000 ms; Follow-through: 1000–2000 ms. ERSP 3D biomechanics of the kick: reliability, movement range and peak acceleration; webcam: Accuracy rate The authors reveal that the right parieto-occipital cluster demonstrated strong alpha desynchronization after the kick, indicating increased visual demands. The mid-frontal cluster revealed theta synchronization before ball contact and alpha desynchronization beginning in the follow-through phase, indicating executive processing and attentional demands Piskin et al. [52] Right posterior and Mid-frontal Clusters (ERSP) AF3, AFz, AF4, F3, F1, Fz, F2 and F4; Pz, P2, P4, P6, P8, POz, PO8, Oz, O2 (MSE); Reference: FCz; Ground: AFz theta (4–7 Hz); Alpha (8–10 Hz); Alpha-2 (11–13 Hz); Beta-1 (14-20Hz) EEGLAB toolbox (version 14.1.2b): remove sinusoidal line noise; band-pass (3 -30Hz); artifacts rejected by 1) remove robust z-score threshold > 5; 2) data were then re- referenced to a common average and downsampled to 256 Hz; 3) the data were epoched from − 3000 to 3000 ms, with baseline correction applied from − 2500 to -500 ms; 4) ICA; 5) All non-brain independent component were subsequently removed from the dataset From − 3000 to 3000 ms, with baseline correction applied from − 2500 to -500 ms ERSP and MSE 3D biomechanics (hip flexion, knee flexion, foot external rotation and acceleration) were digitized and processed for complexity analysis in Matlab (MSE) The authors reveal that injured players exhibit sensorimotor alterations during kicking (differences in posterior alpha and frontal theta oscillations), suggesting compensatory strategies for performing the kicking task, hindering the integration of relevant information Piskin et al. [30] Clusters of brain sources, whose locations occurred in the parieto-occipital and frontal regions; Reference: FCz; Ground: AFz theta (4–7 Hz); Alpha (8–10 Hz); Alpha-2 (11–13 Hz); Beta-1 (14-20Hz) EEGLAB toolbox (version 14.1.2b): remove sinusoidal line noise; band-pass (3 -30Hz); artifacts rejected by 1) remove robust z-score threshold > 5; 2) data were then re- referenced to a common average and downsampled to 256 Hz; 3) the data was epoched to -0-2500 ms from pass onset to focus on post-onset; 4) ICA Post-onset (0–2500 ms) (ERSP) ERSP and MSE 3D biomechanics (hip flexion, knee flexion, foot external rotation and acceleration) were digitized and processed for complexity analysis in Matlab (MSE) The authors revealed that more experienced players exhibit greater passing accuracy, which may indicate the influence of visuospatial and attentional strategies evidenced by alpha parieto-occipital desynchronization and theta frontal synchronization Schmaderer et al. [53] Frontomedial: channel (ch) 11, 17–19; ventrolateral: ch 15–16, 20–21; dorsolateral: ch 1–10, 12–14. nirsLab (2019.4): band pass filter (low cut-off frequency: 0.01 Hz; high cut-off frequency: 0.2 Hz); rejection of channels with variance > 7.5%; The first 45s were considered and divided into three time blooks 15s each; transformation to hemodynamic data Baseline (15s); After the start of the test (15 to 30s) Average oxyhaemoglobin Reactivity and decision-making to visual and auditory stimuli; cognitive decision-making, sustained attention, reactivity, and decision-making in football situations, passes, and heart rate The authors revealed increased activity in the prefrontal region during novel stimuli compared to sport-specific tasks, a result of the automaticity acquired by experts in the field Slutter et al. [54] Motor cortex: channel (ch): 1–4; right prefrontal cortex: ch 9–12; left prefrontal cortex: ch 9–12; left temporal cortex: ch 13–16; right and left dorsolateral prefrontal cortex: ch 17–18, respectively. OxySoft - transform fNIRS signals; Scikit-learn package; Butterworth bandpass (0.02–0.5 Hz); Temporal derivative distribution repair method for artifact rejection; Tukey's biweight function; data were then re- referenced to baseline; rejection of channels with ultra-low FNIRS activity; the last 15 s of the resting period was used to subtract from datapoints; removal of data based on correlation coefficient (> 0.4) between O2Hb and HHb signals per trial-channel Prepatory (-5s) before the researcher's signal O2Hb concentration; average O2Hb activation in each region; Averaged prefrontal cortex activation (left and right); prefrontal cortex asymmetry; connectivity index Small questionnaire: Sport Competition Anxiety Test; Sport Anxiety Scale; Successful and unsuccessful performance of kick; placement and shot power; goalkeeper-looking duration The study revealed distinct brain activation between anxious players (greater activation and asymmetry in the prefrontal cortex and less activation in the motor cortex) and non-anxious players, reinforcing the logic of the neural efficiency theory, which suggests greater activity in relevant areas and suppression of irrelevant ones Note : ERS/ERD = Event-related synchronisation/desynchronization; ERSP = Event-related spectral perturbation; O2Hb = chromophores oxygenated hemoglobin; HHb = deoxygenated hemoglobin; ITC = Inter-trial coherence; MSE = multiscale entropy; 3D = three-dimensional; 2D = two-dimensional; ICA = with independent component analysis. EEG = electroencephalogram; fNIRS = functional near-infrared spectroscopy. -- = information not reported or unclear. ****Insert Table 2 here**** Qualitative synthesis of evidence Table 3 presents the key outcomes derived from the included papers. Firstly, as regarding the EEG studies evaluating ball kicking [28–30, 50–52], four main dependent variables were computed across such investigations. These consisted of i) the event-related spectral perturbation (ERSP), being the most frequently reported outcome (4 of 6 studies; [28, 30, 51, 52]), followed by ii) EEG power (3 studies; [28, 29, 50]), iii) multiscale entropy (MSE; 2 studies; [30, 52]), and less frequently iv) the inter-trial coherence (ITC; 1 study [28]). The early study by Collins et al. [50] reported that in non-expert participants (i.e., non-soccer players), successful attempts (i.e., ball travelled through a 30-centimeter-wide channel) resulted in higher pre-kick alpha power at temporal sites than unsuccessful attempts. Palucci Vieira et al. [28] reported that the kicking velocity was associated with frontal theta power during the impact phase, while kicking accuracy was associated with occipital alpha power during the preparatory phase. In a similar study, Li et al. [29] reported that lower 8–13 Hz power at the frontal and central regions was associated with successful penalty kicks. Piskin et al. [51] initially investigated the measurement error of the EEG to assess ball kicking and reported that the right parieto-occipital cluster demonstrated strong alpha desynchronization after kick, while the mid-frontal cluster revealed theta synchronization before ball contact and alpha desynchronization beginning in the follow-through phase. These variables were shown to have moderate to excellent reliability. Another study by Piskin et al. [30] reported that, compared to novice soccer players, expert soccer players showed greater passing accuracy and exhibited earlier and stronger alpha desynchronization at the right parieto-occipital region prior to ball contact, as well as stronger frontal theta synchronization at ball contact. In a third separate experiment evaluating the role of injuries on kicking-derived EEG signals, Piskin et al. [52] reported differences in cortical activation between participants with injuries and those without injuries; healthy participants exhibited stronger alpha desynchronization post-kick, whereas injured participants showed stronger theta synchronization at the mid-frontal cluster during both the onset of kick and the post-onset period. These findings suggest that injured athletes maintained accuracy by using compensatory strategies for the kicking task, which hindered the integration of relevant information. Finally, regarding the evidence obtained in the studies [53, 54] using specifically the fNIRS technology to evaluate the ball kicking movement, Schmaderer et al. [53] reported that compared to known stimuli (i.e., sport-specific test), unknown or novel stimuli showed significantly higher prefrontal activity while Slutter et al. [54] reported differences in brain activation between anxious and non-anxious players, with anxious players exhibiting greater activation and asymmetry in the prefrontal cortex and lower activation in the motor cortex. The outcomes of the methodological quality assessment using the STROBE checklist is presented in Fig. 2. The overall risk of bias was reported to be low for all the included studies. ****Insert Table 3 here**** ****Insert Fig. 2 here**** Gap map Figure 3 illustrates the interaction between the brain parameters studied (frequency bands and cortical activation measures) and the brain regions explored in the included studies. In addition, Fig. 4 presents the gap mapping according to neuroimaging technologies, brain regions of interest, and key design features of the included studies. The alpha band oscillation was the most researched neural marker, including data in the frontal (n = 5 studies; [28–30, 51, 52]), parietal (n = 5 studies; [28, 30, 50–52]), occipital (n = 4 studies; [28, 30, 51, 52]), and central (n = 3 studies; [28, 29, 50]) brain regions. Limited data were found on alpha band activity in the temporal region (one study; [50]). Theta band oscillations were examined in the frontal, parietal and occipital (n = 4 studies for all the these three regions; [28, 30, 51, 52]), while available data on theta band were limited in the central region (one study; [28]). The beta frequency band (including the beta-1 sub-band, 14–20 Hz) was also investigated in the frontal, parietal, and occipital regions (n = 4 studies each; [28, 30, 51, 52]), and with limited evidence in the central region (one study; [28]). For delta and gamma frequency bands, only limited data were found, each examined in a single study [28] in the frontal, central, parietal, and occipital regions. Cortical oxygenation (fNIRS) was studied in the frontal cortex (two studies; [53, 54]) and to a limited extent in the motor cortex (one study; [54]), with no fNIRS data available for the parietal or occipital regions. Regarding population characteristics and study methodology, results for experienced players during ball passing drills were the most researched (3 studies; [30, 51, 52]). Some data were also available for penalty kicks with experienced players (2 studies; [29, 53]), while limited data existed on target kicking with novice participants (1 study; [50]), and maximum velocity instep kicks with young players (1 study; [28]). No data were found (Fig. 4B) for exclusively female samples, professional/high-level or elite participants, samples of children or pre-adolescents, or for game-play conditions during brain signal acquisition. The majority of studies (6 out of 8) did not include any opponents [29, 30, 50–53], and only two studies included goalkeepers as opponents [28, 54]. On-field testing conditions were also scarce (2 studies; [28, 54]) while more studies were available using laboratory environments. Only limited evidence on the use of non-preferred limb was identified (1 study; [50]). As concerning the data acquisition technology, six studies employed EEG [28–30, 50–52], while two used fNIRS [53, 54] as their primary neuroimaging modality. No study combined both technologies simultaneously. Additional technologies were used to measure kinematics in five studies [28, 30, 51, 52, 54], while psychological measures (e.g., anxiety) were assessed in only one study [54]. Finally, only one study [51] formally evaluated the reliability of EEG signal measurements during the ball kicking task (Fig. 4C), as no equivalent reliability data are available for fNIRS-based protocols. ****Insert Fig. 3 here**** ****Insert Fig. 4 here**** DISCUSSION This systematic review synthesized evidence on brain-derived signals measured during soccer ball-kicking tasks and related them to performance outcomes and contextual constraints. Convergent findings indicate that successful and/or higher-quality kicking performance is accompanied by phase-specific cortical dynamics, particularly within frontal (attentional/motor-programming), sensorimotor/central (motor control), and parieto-occipital (visuospatial) regions. In EEG studies, performance was most consistently linked to modulations in theta and alpha bands across distinct phases of the kick (preparation, approach/execution, follow-through), whereas fNIRS studies emphasized the role of prefrontal and motor-cortex oxygenation patterns under pressure/anxiety or varying cognitive demands. Using the gap map method, it was possible to observe that currently there are no literature studies using EEG–fNIRS as part of the experimental paradigm. Evidence concerning data on measurement error (e.g., reliability) was also limited across studies. In the following paragraphs we will offer interpretations to the main findings of the present systematic review as well as possible directives for future investigations. Neurophysiological correlates of kicking success and performance outcomes In penalty-kick contexts, successful trials were associated with lower 8–13 Hz power in frontal and central regions during motor preparation (approximately the final 2 s pre-kick), suggesting a more efficient pre-action state and reduced costly cortical engagement for planning/control when execution is successful [ 29 ]. These results align with efficiency-oriented interpretations in which skilled performance is supported by selective recruitment of task-relevant networks without excessive prefrontal/sensorimotor activation that might reflect conscious control or maladaptive attentional capture [ 29 ]. Evidence also links brain signals to continuous performance dimensions (velocity and accuracy), rather than binary success alone. In youth sub-elite players performing instep shots from longer distance with a goalkeeper present, frontal theta activity was associated with ball velocity, whereas occipital alpha activity during preparation was associated with accuracy (mean radial error) [ 28 ]. These associations map well onto a dual-demand structure of soccer kicking. Generating ball speed requires coordinated high-force multi-joint sequencing that may depend on frontal/cognitive control signals at critical instants, while accuracy depends heavily on visuospatial processing and stabilization of perceptual information (indexed here by posterior alpha modulation). Importantly, early evidence from a foundational study using a simpler task suggested that, in non-soccer participants, greater pre-kick alpha at temporal sites predicted successful execution [ 50 ]. While that early result should be interpreted cautiously due to task simplicity and participant characteristics, it suggests that successful kicking can be preceded by measurable oscillatory differences even in relatively novice performers, albeit with topographies and interpretations that may differ from later mobile-neuroimaging paradigms. Of note, one area underexplored in the studies evaluating the role of brain signals in the kinematics and outcomes of ball kicking refers to the asymmetries commonly observed. While there is extensive evidence as concerning the presence of asymmetry in the kinematics and outcomes of ball kicking, favoring the dominant side across investigations including various ages, genders and playing levels [ 56 – 61 ], the central mechanisms likely involved remain unclear. This is confirmed here since among the studies included in the present systematic review, only one considered kicks with the dominant and non-dominant limbs (Table 2 ); in this isolated study authors provided a brief mention that there was no main effect for preferred vs. non-preferred kicking limb on brain-derived signals computed as well as it was not one of the objectives of the such study to evaluate asymmetries [ 50 ], thus implying existence of only limited evidence regarding this issue. Visuospatial and attentional strategies indexed by posterior alpha and frontal theta dynamics In a reliability-focused EEG study using source-derived approaches, consistent parieto-occipital (alpha desynchronization) and mid-frontal (theta synchronization) dynamics were observed across sessions, supporting their candidacy as stable measure of directed pass-kicks [ 51 ]. Extending this, expertise comparisons showed that experienced players demonstrated higher pass accuracy alongside earlier/stronger parieto-occipital alpha desynchronization prior to ball contact and stronger frontal theta synchronization around ball contact [ 30 ]. These findings are consistent with an expertise-related refinement of visuospatial attention (posterior alpha) and task-focused control/monitoring (frontal theta), potentially reflecting more efficient allocation of resources to extract target information and stabilize the sensorimotor plan. Complementary evidence from fNIRS further supports the broader principle that familiarity/automaticity reduces prefrontal load. In a sample of semi-professional players, sport-specific (familiar) cognitive tasks elicited lower prefrontal activity changes than general (more novel) cognitive tasks, consistent with the interpretation that learned automatisms reduce reliance on effortful prefrontal processing [ 53 ]. Although this study assessed perceptual-cognitive tasks rather than solely biomechanics-focused kicking outcomes, it provides converging support suggesting that when task demands are familiar and well-trained, the system may achieve performance with lower prefrontal control signatures. Pressure, anxiety, and injury as perturbations of neural efficiency during kicking In an ecologically oriented penalty study using fNIRS, anxiety was associated with higher prefrontal activation and altered lateral asymmetry, alongside lower activation in the motor cortex; these patterns were linked to missed penalties and to anxious states, consistent with a choking-under-pressure interpretation emphasizing task-irrelevant prefrontal engagement [ 54 ]. Injury status (ACL reconstruction) similarly appears to be associated with altered cortical dynamics and movement variability during target-directed kicking. In the included injury comparison study, injured players exhibited distinct posterior alpha and frontal theta oscillatory patterns relative to healthy players, interpreted as compensatory attentional strategies that may support maintained accuracy at the expense of efficient visuospatial integration [ 52 ]. Possibly, soccer kicking can be understood not only as a biomechanical skill but also as a neurophysiological behavior that may be reorganized after injury, potentially contributing to persistent performance deficits or altered coordination strategies even when athletes have returned to play [ 52 ]. Limitations and future research Currently, it is not uncommon to observe elite soccer players taking penalty kicks with a small jump just before contacting the ball [ 62 ], potentially aiming to delay their final movement and possibly make an online adjustment to the direction of the kick based on the goalkeeper's movements. This could in some ways contradict part of the literature that indicates that information about which side the goalkeeper decides to dive should be obtained by the penalty taker early in the approach run [ 63 ]. Among the studies included here, only one indicated that participating players could adopt an approach run with varying speeds (i.e. slow down) if they wanted to [ 54 ]. However, to date there have been no studies that have analyzed brain signals specifically in relation to this contemporary type of kicking strategy using small jump/hop immediately before ball contact in shooting. Another important limitation identified in the present review study is that a goalkeeper attempting to block shots was used in only two of the included studies (see Table 2 ). This might reduce the usefulness of the evidence collated here to “real-world” conditions. In fact, previous experimental studies indicate that unopposed testing scenarios have only limited value for predicting actual game performance in terms of ball kicking ability [ 64 – 66 ]. Notwithstanding, the role of vision has been also widely documented for having a direct impact on the outcomes of kicks in football [ 67 – 69 ]. Meanwhile, according to the present systematic review only half of the literature studies considered brain-derived measurements collected from the occipital region. In short, the principal limitation of the current evidence base is its relatively small size (eight original research articles published until the date of the searches) and high heterogeneity across tasks, contexts, technologies, and analytic methods, which precluded quantitative synthesis and limits the confidence of mechanistic claims. There is substantial diversity in tasks (short pass-kicks vs. long instep shots vs. penalties), environments (laboratory vs. artificial pitch vs. natural grass), and opponent presence, all of which likely modulate cortical demand profiles and complicate cross-study synthesis. One potential explanation for the small number of studies until the time of writing of this review is that the proposed experiments were probably only made possible by the most recent technological advances in the area (i.e. 88% of studies published after 2020 – see Table 1 ); in fact, until shortly before that (in 2018), it was reported by Perrey and Besson [ 70 ] an extreme difficulty in dealing with signal quality issues using both methodologies (EEG and fNIRS) in sports contexts. Future research should use more standardized task taxonomies with explicit manipulation of ecological constraints, preregister primary neurophysiological hypotheses to reduce analytic flexibility, expand beyond sub-elite samples and include women and youth players, and converge on harmonized reporting for mobile EEG/fNIRS in sport movements, including artifact quantification and sensitivity analyses. Practical applications The emerging evidence suggests that training and rehabilitation programs may benefit from the cognitive–sensorimotor processes indexed by frontal theta and posterior alpha dynamics. For performance training, coaches and practitioners could integrate practice designs that promote stable visuospatial attention and automated execution under accuracy constraints, while systematically introducing pressure elements to reduce maladaptive prefrontal over-engagement associated with anxiety and missed penalties [ 54 ]. For injury rehabilitation, monitoring of task-related cortical dynamics and movement variability during standardized pass-kick tasks may help identify compensatory attentional strategies and guide progression toward more efficient visuospatial–sensorimotor integration before full return to competition [ 52 ]. CONCLUSIONS Evidence from a small but growing literature indicates that soccer ball-kicking performance is associated with measurable, phase-specific cortical dynamics, most consistently involving frontal theta and parieto-occipital alpha modulations during preparation and execution. Across studies, better performance and expertise tend to align with patterns consistent with efficient attentional allocation and visuospatial processing, whereas anxiety and injury contexts appear to shift cortical engagement toward potentially compensatory, less efficient control strategies. While these findings are interesting, they remain preliminary due to limited study numbers, modest sample sizes, and heterogeneous methods. Given the complementary nature of temporal (EEG) and spatial (fNIRS) resolutions, the current systematic review with gap map identified that future research should attempt to evaluate ball kicking using both technologies concomitantly, as none of the studies included here has done this type of analysis until the moment. Declarations Ethics approval Not applicable. All articles included in the current review reported respective ethical aspects adopted for data collection – this was defined in the protocol as an inclusion criterion. The protocol of the review was registered in the OSF Preregistration - https://osf.io/nzasb/overview (DOI: https://doi.org/10.17605/OSF.IO/NZASB; Date created/registered: Jun 9, 2025). Consent for publication Not applicable. Competing interests The authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper. Funding Luiz H. Palucci Vieira: ongoing assignment received from PUCP - Tenure Track program. No author has any financial interest or received any financial benefit from this research. In addition, the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. Acknowledgements The authors are thankful to the information made available by the Sistema de Bibliotecas-PUCP - https://biblioteca.pucp.edu.pe/ (accessed on 13 October 2025). We would also like to thank Fabio Barbieri for his suggestions in the early stage of the project. Availability of data and materials Similarity and AI writing reports for the present paper can be found for download in https://doi.org/10.5281/zenodo.18716878. All raw data supporting this systematic review are derived from previously published studies, which have been cited in the text and reference list. Additional processed data that support the findings of the current review are available from the corresponding author on reasonable request. Authorship Contributions NU and LV designed the research. LV supervised the research activity planning and execution. NU and FZ conducted the literature search and screening steps. MF, FLC and NU conducted the data extraction and methodological quality evaluation, which were verified by LV. NU, FMC, RT, RKT, MF, FLC, FZ and LV interpreted the data analysis. FMC, RT, RKT and LV wrote the first draft of the manuscript with critical input from NU, MF, FLC and FZ. All authors read and approved the final manuscript. References Stafylidis A, Mandroukas A, Michailidis Y, Metaxas TI. Decoding Success: Predictive Analysis of UEFA Euro 2024 to Uncover Key Factors Influencing Soccer Match Outcomes. Appl Sci. 2024;14:7740. https://doi.org/10.3390/app14177740 . Kubayi A, Larkin P. Match-Related Statistics Differentiating Winning and Losing Teams at the 2019 Africa Cup of Nations Soccer Championship. Front Sports Act Living. 2022;4. https://doi.org/10.3389/fspor.2022.807198 . Varley MC, Gregson W, McMillan K, Bonanno D, Stafford K, Modonutti M, et al. Physical and technical performance of elite youth soccer players during international tournaments: influence of playing position and team success and opponent quality. Sci Med Footb. 2017;1:18–29. https://doi.org/10.1080/02640414.2016.1230676 . Oliva-Lozano JM, Yousefian F, Chmura P, Gabbett TJ, Cost R. Analysis of FIFA 2023 Women’s World Cup match performance according to match outcome and phase of the tournament. Biol Sport. 2024;42:71–84. https://doi.org/10.5114/biolsport.2025.142643 . Kubayi A, Larkin P. Technical performance of soccer teams according to match outcome at the 2019 FIFA Women’s World Cup. Int J Perform Anal Sport. 2020;20:908–16. https://doi.org/10.1080/24748668.2020.1809320 . de Jong LMS, Gastin PB, Angelova M, Bruce L, Dwyer DB. Technical determinants of success in professional women’s soccer: A wider range of variables reveals new insights. PLoS ONE. 2020;15:e0240992. https://doi.org/10.1371/journal.pone.0240992 . Lees A, Asai T, Andersen TB, Nunome H, Sterzing T. The biomechanics of kicking in soccer: a review. J Sports Sci. 2010;28:805–17. https://doi.org/10.1080/02640414.2010.481305 . Lees A, Nolan L. The biomechanics of soccer: a review. J Sports Sci. 1998;16:211–34. https://doi.org/10.1080/026404198366740 . Kellis E, Katis A. Biomechanical characteristics and determinants of instep soccer kick. J Sports Sci Med. 2007;6:154–65. Shan G, Zhang X. From 2D leg kinematics to 3D full-body biomechanics-the past, present and future of scientific analysis of maximal instep kick in soccer. Sports Med Arthrosc Rehabil Ther Technol. 2011;3:23. https://doi.org/10.1186/1758-2555-3-23 . Chow JW, Knudson DV. Use of deterministic models in sports and exercise biomechanics research. Sports Biomech. 2011;10:219–33. https://doi.org/10.1080/14763141.2011.592212 . Palucci Vieira LH, Barbieri FA, Kellis E, Oliveira L, Aquino R, Cunha S, et al. Organisation of instep kicking in young U11 to U20 soccer players. Sci Med Footb. 2021;5:111–20. https://doi.org/10.1080/24733938.2020.1807043 . De Witt JK, Hinrichs RN. Mechanical factors associated with the development of high ball velocity during an instep soccer kick. Sports Biomech. 2012;11:382–90. https://doi.org/10.1080/14763141.2012.661757 . Palucci Vieira LH, Santinelli FB, Carling C, Kellis E, Santiago PRP, Barbieri FA. Acute Effects of Warm-Up, Exercise and Recovery-Related Strategies on Assessments of Soccer Kicking Performance: A Critical and Systematic Review. Sports Med. 2021;51:661–705. https://doi.org/10.1007/s40279-020-01391-9 . Palucci Vieira LH. Holistic approach to testing ball kicking mechanics and outcome metrics in soccer: Methodological aspects, observation and intervention (PhD Academy Award). Br J Sports Med. 2024;58:345–7. Perrey S, Besson P. Studying brain activity in sports performance: Contributions and issues. Prog Brain Res. 2018;240:247–67. https://doi.org/10.1016/bs.pbr.2018.07.004 . Sanes JN, Donoghue JP. Plasticity and primary motor cortex. Annu Rev Neurosci. 2000;23:393–415. https://doi.org/10.1146/annurev.neuro.23.1.393 . Karni A, Meyer G, Rey-Hipolito C, Jezzard P, Adams MM, Turner R, et al. The acquisition of skilled motor performance: fast and slow experience-driven changes in primary motor cortex. Proc Natl Acad Sci U S A. 1998;95:861–8. https://doi.org/10.1073/pnas.95.3.861 . Shadmehr R, Smith MA, Krakauer JW. Error correction, sensory prediction, and adaptation in motor control. Annu Rev Neurosci. 2010;33:89–108. https://doi.org/10.1146/annurev-neuro-060909-153135 . Shibasaki H, Hallett M. What is the Bereitschaftspotential? Clin Neurophysiol. 2006;117:2341–56. https://doi.org/10.1016/j.clinph.2006.04.025 . Neuper C, Wörtz M, Pfurtscheller G. ERD/ERS patterns reflecting sensorimotor activation and deactivation. Prog Brain Res. 2006;159:211–22. https://doi.org/10.1016/S0079-6123(06)59014-4 . Pfurtscheller G, Neuper C, Andrew C, Edlinger G. Foot and hand area mu rhythms. Int J Psychophysiol. 1997;26:121–35. https://doi.org/10.1016/s0167-8760(97)00760-5 . Makeig S, Gramann K, Jung T-P, Sejnowski TJ, Poizner H. Linking brain, mind and behavior. Int J Psychophysiol. 2009;73:95–100. https://doi.org/10.1016/j.ijpsycho.2008.11.008 . Gramann K, Ferris DP, Gwin J, Makeig S. Imaging natural cognition in action. Int J Psychophysiol. 2014;91:22–9. https://doi.org/10.1016/j.ijpsycho.2013.09.003 . Gorjan D, Gramann K, De Pauw K, Marusic U. Removal of movement-induced EEG artifacts: current state of the art and guidelines. J Neural Eng. 2022;19. https://doi.org/10.1088/1741-2552/ac542c . Gwin JT, Gramann K, Makeig S, Ferris DP. Removal of movement artifact from high-density EEG recorded during walking and running. J Neurophysiol. 2010;103:3526–34. https://doi.org/10.1152/jn.00105.2010 . Snyder KL, Kline JE, Huang HJ, Ferris DP. Independent Component Analysis of Gait-Related Movement Artifact Recorded using EEG Electrodes during Treadmill Walking. Front Hum Neurosci. 2015;9. https://doi.org/10.3389/fnhum.2015.00639 . Palucci Vieira LH, Carling C, da Silva JP, Santinelli FB, Polastri PF, Santiago PRP, et al. Modelling the relationships between EEG signals, movement kinematics and outcome in soccer kicking. Cogn Neurodyn. 2022;16:1303–21. https://doi.org/10.1007/s11571-022-09786-2 . Li D, Elbanna H, Lin F-Y, Lu C-J, Chen L-J, Lu G, et al. Neuromotor mechanisms of successful football penalty kicking: an EEG pilot study. Front Psychol. 2025;16:1452443. https://doi.org/10.3389/fpsyg.2025.1452443 . Piskin D, Müller R, Büchel D, Lehmann T, Baumeister J. Behavioral and cortical dynamics underlying superior accuracy in short-distance passes. Behav Brain Res. 2024;471:115120. https://doi.org/10.1016/j.bbr.2024.115120 . Jacobsen NSJ, Blum S, Witt K, Debener S. A walk in the park? Characterizing gait-related artifacts in mobile EEG recordings. Eur J Neurosci. 2021;54:8421–40. https://doi.org/10.1111/ejn.14965 . Seynaeve M, Mantini D, de Beukelaar TT. Electrophysiological Approaches to Understanding Brain-Muscle Interactions During Gait: A Systematic Review. Bioeng (Basel). 2025;12:471. https://doi.org/10.3390/bioengineering12050471 . Perrey S. Non-invasive NIR spectroscopy of human brain function during exercise. Methods. 2008;45:289–99. https://doi.org/10.1016/j.ymeth.2008.04.005 . Herold F, Wiegel P, Scholkmann F, Thiers A, Hamacher D, Schega L. Functional near-infrared spectroscopy in movement science: a systematic review on cortical activity in postural and walking tasks. Neurophotonics. 2017;4:041403. https://doi.org/10.1117/1.NPh.4.4.041403 . Menant JC, Maidan I, Alcock L, Al-Yahya E, Cerasa A, Clark DJ, et al. A consensus guide to using functional near-infrared spectroscopy in posture and gait research. Gait Posture. 2020;82:254–65. https://doi.org/10.1016/j.gaitpost.2020.09.012 . Birbaumer N, Weber C, Neuper C, Buch E, Haapen K, Cohen L. Physiological regulation of thinking: brain-computer interface (BCI) research. Prog Brain Res. 2006;159:369–91. https://doi.org/10.1016/S0079-6123(06)59024-7 . Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst Reviews. 2021;10:89. https://doi.org/10.1186/s13643-021-01626-4 . Sarmento H, Afonso J, Clemente F, Gouveia ÉR, Ordoñez-Saavedra N, Silva J, et al. Unlocking the power of set pieces in men’s professional football - a scoping review. Int J Sports Med. 2025. https://doi.org/10.1055/a-2563-0323 . Hamacher D, Herold F, Wiegel P, Hamacher D, Schega L. Brain activity during walking: A systematic review. Neurosci Biobehavioral Reviews. 2015;57:310–27. https://doi.org/10.1016/j.neubiorev.2015.08.002 . Palucci Vieira LH, Clemente FM, Silva RM, Vargas-Villafuerte KR, Carpes FP. Measurement Properties of Wearable Kinematic-Based Data Collection Systems to Evaluate Ball Kicking in Soccer: A Systematic Review with Evidence Gap Map. Sens (Basel). 2024;24:7912. https://doi.org/10.3390/s24247912 . Higgins J, Thomas J, Chandler J, Cumpston M, Li T, Page M, et al. Cochrane Handbook for Systematic Reviews of Interventions. Online: The Cochrane Collaboration; 2024. Winter EM, Maughan RJ. Requirements for ethics approvals. J Sports Sci. 2009;27:985–985. https://doi.org/10.1080/02640410903178344 . Vergnes J-N, Marchal-Sixou C, Nabet C, Maret D, Hamel O. Ethics in systematic reviews. J Med Ethics. 2010;36:771–4. https://doi.org/10.1136/jme.2010.039941 . Vitorio R, Stuart S, Rochester L, Alcock L, Pantall A. fNIRS response during walking - Artefact or cortical activity? A systematic review. Neurosci Biobehav Rev. 2017;83:160–72. https://doi.org/10.1016/j.neubiorev.2017.10.002 . von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370:1453–7. https://doi.org/10.1016/S0140-6736(07)61602-X . O’Reilly M, Caulfield B, Ward T, Johnston W, Doherty C. Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review. Sports Med. 2018;48:1221–46. https://doi.org/10.1007/s40279-018-0878-4 . Rico-González M, Pino-Ortega J, Méndez A, Clemente FM, Baca A. Machine learning application in soccer: a systematic review. Biol Sport. 2023;40:249–63. https://doi.org/10.5114/biolsport.2023.112970 . McGuinness LA, Higgins JPT. Risk-of-bias VISualization (robvis): An R package and Shiny web app for visualizing risk-of-bias assessments. Res Synthesis Methods. 2021;12:55–61. https://doi.org/10.1002/jrsm.1411 . Palucci Vieira LH, Clemente FM, Chang Marquez FA, Rea Olivares WM, Vargas Villafuerte KR, Carpes FP. Accuracy Standards of Wearable Technologies for Assessment of Soccer Kicking: Protocol for a Systematic Literature Review. JMIR Res Protoc. 2024;13:e57433. https://doi.org/10.2196/57433 . Collins D, Powell G, Davies I. Cerebral activity prior to motion task performance: an electroencephalographic study. J Sports Sci. 1991;9:313–24. https://doi.org/10.1080/02640419108729892 . Piskin D, Büchel D, Lehmann T, Baumeister J. Reliable electrocortical dynamics of target-directed pass-kicks. Cogn Neurodyn. 2024;18:2343–57. https://doi.org/10.1007/s11571-024-10094-0 . Piskin D, Cobani G, Lehmann T, Büchel D, Baumeister J. Cortical changes associated with an anterior cruciate ligament injury may retrograde skilled kicking in football: preliminary EEG findings. Sci Rep. 2025;15:2208. https://doi.org/10.1038/s41598-025-86196-4 . Schmaderer LF, Meyer M, Reer R, Schumacher N. What happens in the prefrontal cortex? Cognitive processing of novel and familiar stimuli in soccer: An exploratory fNIRS study. Eur J Sport Sci. 2023;23:2389–99. https://doi.org/10.1080/17461391.2023.2238699 . Slutter MWJ, Thammasan N, Poel M. Exploring the Brain Activity Related to Missing Penalty Kicks: An fNIRS Study. Frontier Comput Sci. 2021;3. https://doi.org/10.3389/fcomp.2021.661466 . Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods. 2004;134:9–21. https://doi.org/10.1016/j.jneumeth.2003.10.009 . Hunter AH, Smith NMA, Camata TV, Crowther MS, Mather A, Souza NM, et al. Age- and size-corrected kicking speed and accuracy in elite junior soccer players. Sci Med Footb. 2022;6:29–39. https://doi.org/10.1080/24733938.2021.1899274 . Teixeira MCT, Teixeira LA. Leg preference and interlateral performance asymmetry in soccer player children. Dev Psychobiol. 2008;50:799–806. https://doi.org/10.1002/dev.20322 . Carlsson T, Isberg J, Nilsson J, Carlsson M. The Influence of Task Conditions on Side Foot-Kick Accuracy among Swedish First League Women’s Soccer Players. J Sports Sci Med. 2018;17:74–81. Barfield WR, Kirkendall DT, Yu B. Kinematic instep kicking differences between elite female and male soccer players. J Sports Sci Med. 2002;1:72–9. Outram T, Freeman H, Briley S. The effect of leg dominance on the frequency and 3D kinematics of soccer passing in female academy players. Paris, France: European College of Sports Science; 2023. Vieira LHP, de Souza Serenza F, de Andrade VL, de Paula Oliveira L, Mariano FP, Santana JE, et al. Kicking Performance and Muscular Strength Parameters with Dominant and Nondominant Lower Limbs in Brazilian Elite Professional Futsal Players. J Appl Biomech. 2016;32:578–85. https://doi.org/10.1123/jab.2016-0125 . Zheng R, van der Kamp J, Miller-Dicks M, Navia J, Savelsbergh G. The effectiveness of penalty takers’ deception: A scoping review. Hum Mov Sci. 2023;90:103122. https://doi.org/10.1016/j.humov.2023.103122 . Navarro M, van der Kamp J, Ranvaud R, Savelsbergh GJP. The mere presence of a goalkeeper affects the accuracy of penalty kicks. J Sports Sci. 2013;31:921–9. https://doi.org/10.1080/02640414.2012.762602 . Serpiello FR, Cox A, Oppici L, Hopkins WG, Varley MC. The Loughborough Soccer Passing Test has impractical criterion validity in elite youth football. Sci Med Footb. 2017;1:60–4. https://doi.org/10.1080/02640414.2016.1254810 . Ré AHN, Cattuzzo TM, Santos FMC, Monteiro CBM. Anthropometric characteristics, field test scores and match-related technical performance in youth indoor soccer players with different playing status. Int J Perform Anal Sport. 2014;14:482–92. https://doi.org/10.1080/24748668.2014.11868737 . Orth D, Davids K, Araújo D, Renshaw I, Passos P. Effects of a defender on run-up velocity and ball speed when crossing a football. Eur J Sport Sci. 2014;14(Suppl 1):S316–323. https://doi.org/10.1080/17461391.2012.696712 . Nagano T, Kato T, Fukuda T. Visual Behaviors of Soccer Players While Kicking with the inside of the Foot. Percept Mot Skills. 2006;102:147–56. https://doi.org/10.2466/pms.102.1.147-156 . Noel B, Van Der Kamp J. Gaze behaviour during the soccer penalty kick: an investigation of the effects of strategy and anxiety. Int J Sport Psycol. 2012;43:326. Piras A, Vickers JN. The effect of fixation transitions on quiet eye duration and performance in the soccer penalty kick: instep versus inside kicks. Cogn Process. 2011;12:245–55. https://doi.org/10.1007/s10339-011-0406-z . Perrey S, Besson P. Studying brain activity in sports performance: Contributions and issues. Prog Brain Res. 2018;240:247–67. https://doi.org/10.1016/bs.pbr.2018.07.004 . Table 2 Table 2 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1PRISMA2020checklist.pdf Table2.docx Cite Share Download PDF Status: Published Journal Publication published 01 Apr, 2026 Read the published version in BMC Sports Science, Medicine and Rehabilitation → Version 1 posted Editorial decision: Revision requested 12 Mar, 2026 Reviewers agreed at journal 11 Mar, 2026 Reviews received at journal 11 Mar, 2026 Reviews received at journal 10 Mar, 2026 Reviewers agreed at journal 10 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviewers agreed at journal 08 Mar, 2026 Reviewers agreed at journal 08 Mar, 2026 Reviewers agreed at journal 08 Mar, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviewers invited by journal 06 Mar, 2026 Editor assigned by journal 05 Mar, 2026 Submission checks completed at journal 04 Mar, 2026 First submitted to journal 26 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8940911","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":604621989,"identity":"052887e7-3ad5-4de8-ad8c-175c055c8767","order_by":0,"name":"Nicole Unsihuay","email":"","orcid":"","institution":"Pontificia Universidad Católica del Perú","correspondingAuthor":false,"prefix":"","firstName":"Nicole","middleName":"","lastName":"Unsihuay","suffix":""},{"id":604621990,"identity":"739275ce-030a-4b60-b416-e2a1edeca0b9","order_by":1,"name":"Filipe Manuel Clemente","email":"","orcid":"","institution":"Gdansk University of Physical Education and Sport","correspondingAuthor":false,"prefix":"","firstName":"Filipe","middleName":"Manuel","lastName":"Clemente","suffix":""},{"id":604621991,"identity":"7d2a13a9-7cc5-4f77-b73c-46f7269ce4d5","order_by":2,"name":"Robert Trybulski","email":"","orcid":"","institution":"Wojciech Korfanty Upper Silesian Academy in Katowice","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Trybulski","suffix":""},{"id":604621992,"identity":"1f98cfc2-2466-4a7e-8c97-4069282f0f07","order_by":3,"name":"Rohit Kumar Thapa","email":"","orcid":"","institution":"Symbiosis International (Deemed University)","correspondingAuthor":false,"prefix":"","firstName":"Rohit","middleName":"Kumar","lastName":"Thapa","suffix":""},{"id":604621993,"identity":"3c385b08-820e-4002-810f-59ed4b076b79","order_by":4,"name":"Murilo Henrique Faria","email":"","orcid":"","institution":"São Paulo State University (UNESP)","correspondingAuthor":false,"prefix":"","firstName":"Murilo","middleName":"Henrique","lastName":"Faria","suffix":""},{"id":604621994,"identity":"07d5cdb3-011a-4e83-9273-c5305fb659f7","order_by":5,"name":"Fanny Lys Casado","email":"","orcid":"","institution":"Pontificia Universidad Católica del Perú","correspondingAuthor":false,"prefix":"","firstName":"Fanny","middleName":"Lys","lastName":"Casado","suffix":""},{"id":604621995,"identity":"629679aa-d861-4803-a917-e3e6a7829c22","order_by":6,"name":"Fernando Zvietcovich","email":"","orcid":"","institution":"Pontificia Universidad Católica del Perú","correspondingAuthor":false,"prefix":"","firstName":"Fernando","middleName":"","lastName":"Zvietcovich","suffix":""},{"id":604621996,"identity":"7aa4a963-f96f-4c29-9be6-f82db755a07a","order_by":7,"name":"Luiz H. Palucci Vieira","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYBACPgbGBhAtAyIOfICKSuDTwgbVwgMiDs4gTgsEgLUw8xClRSK5+QPjDjseg+OHHx623XFY3pyB+eBtHoY7cri1JDYYMJ5J5jE4k2ZwOPfMYcOdDWzJ1jwMz4zxaUlgbGPmMbjBw3A4t+1wgsEBHjNpIDuxAY+WA4xt9RAtlmAt/N9AWurxaGlsYGw7DNHCCLGFDaQlAafDeB42MySeOc4jCfTLwd4z6YY7m9mMLecYPDPEZQs/e/rjDx93VMvxHT/8+MPPHdby5uzND2+8qbgjj8sWMID7FBStBswglsEBvDqgCQCmBcIkoGUUjIJRMApGEgAAvnpUnmujO2sAAAAASUVORK5CYII=","orcid":"","institution":"Pontificia Universidad Católica del Perú","correspondingAuthor":true,"prefix":"","firstName":"Luiz","middleName":"H. Palucci","lastName":"Vieira","suffix":""}],"badges":[],"createdAt":"2026-02-22 18:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8940911/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8940911/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13102-026-01676-y","type":"published","date":"2026-04-01T15:58:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":104547493,"identity":"35bf91c6-5f74-42c9-ad0d-b641f77cacc9","added_by":"auto","created_at":"2026-03-13 07:37:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1179791,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA 2020 flow diagram for the current systematic review on brain activity and ball kicking.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8940911/v1/13e93a535b390bda44310f26.png"},{"id":104781743,"identity":"89e746ec-6a9a-4473-9d63-a39dcd6200f4","added_by":"auto","created_at":"2026-03-17 07:56:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1455618,"visible":true,"origin":"","legend":"\u003cp\u003eOutcomes of the methodological quality assessment for each included study.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8940911/v1/dcd15b37010186523fd21450.png"},{"id":104782222,"identity":"e8338fa1-7f5c-421b-8f7a-3d3617913ffc","added_by":"auto","created_at":"2026-03-17 07:56:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":360034,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction between the brain-derived outcomes investigated and the brain regions explored in the included studies. Open circles represent the number of studies. Shaded cells indicate evidence from ≥ 3 studies. – = No evidence available.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8940911/v1/fd7a034883f4390231d156d0.png"},{"id":104547501,"identity":"1a962c49-4bb8-4846-8086-273f20510941","added_by":"auto","created_at":"2026-03-13 07:37:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":649861,"visible":true,"origin":"","legend":"\u003cp\u003eEvidence gap map regarding the neuroimaging technologies, brain regions, and key study design features across the included studies. Open circles represent the number of studies found. Shaded cells indicate evidence from ≥ 3 studies. – = No evidence available.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8940911/v1/b1f65758f92c741b627d680c.png"},{"id":106344409,"identity":"fc13b255-6f06-49c2-8aa3-a87ea44b3382","added_by":"auto","created_at":"2026-04-07 16:14:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5710855,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8940911/v1/6e2f06c8-3255-4b00-80f8-6d7882568362.pdf"},{"id":104547480,"identity":"1d9163c3-f87c-4495-a24f-ab6bba062ac4","added_by":"auto","created_at":"2026-03-13 07:37:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":124923,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1PRISMA2020checklist.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8940911/v1/009d2dbb5cb411b8692adc28.pdf"},{"id":104781381,"identity":"e7f3fdfd-928c-4403-af1a-e19a41330ce3","added_by":"auto","created_at":"2026-03-17 07:55:34","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":27329,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8940911/v1/af510a0b1eb08953edf3d1db.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eBrain-derived Signals Related to Ball Kicking Movement in Soccer and Technologies Employed: A Systematic Literature Review With Gap Map\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eBall kicking has been a determinant variable of match performance (i.e. outcomes) in male and female youth or senior soccer, given the strong influence of shooting on target upon winning results [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], motivating extensive scientific analysis of the movement [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Indeed, the kicking action consists in the most widely studied skill across the biomechanics of soccer literature, reflecting its technical importance and recurrent use across playing situations [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Ball kicking is characterized as a multiarticular ballistic action, involving proximal-to-distal motion sequencing of the lower-limb segments that may directly influences on the development of foot velocity and ball placement-derived outcomes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Throughout the history of biomechanical - and motor control - analysis of kicking in soccer, the methodological progress from 2D (two-dimensional) kinematics toward 3D (three-dimensional) full-body biomechanics has improved descriptive and explanatory models of kicking, while simultaneously emphasizing the need to capture movement coordination across the whole body and task phases [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Deterministic models [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] have indeed been previously tested regarding the mechanical factors related to ball kicking outcomes in both youth and senior players [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Nevertheless, even though there is a mature knowledge base on the characterization of biomechanical features and the effects of individual and environmental constraints upon soccer ball kicking [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], understanding of brain\u0026ndash;behavior relationships in ecologically valid sports actions remains comparatively limited, particularly for high-speed skills executed under accuracy and pressure constraints [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSkilled motor performance depends on distributed central cortical networks that support voluntary movement control and exhibit experience-dependent plasticity [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In general, motor skill acquisition occurs through time-dependent stages that include rapid within-session gains and later consolidation processes that stabilize performance [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Furthermore, accurate skilled actions further rely on adaptive internal models that use sensory prediction errors to attempt refine motor commands and maintain calibration [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In electrophysiological terms, self-initiated actions are preceded by the Bereitschaftspotential (i.e. early cortical activation preceding self-initiated movements), reflecting preparatory activity across medial and lateral motor areas with temporospatial structure related to movement generation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Complementarily, event-related desynchronization/synchronization in mu (standard alpha band) and beta rhythms provides time-resolved indices of sensorimotor activation and inhibition during preparation, execution, and imagery [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Lower-limb movements also exhibit somatotopically organized oscillatory dynamics over sensorimotor regions, underscoring the feasibility of tracking leg-related cortical processes using scalp electroencephalogram (EEG) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSince many neuroimaging paradigms constrain natural movement, mobile brain/body imaging concepts have been proposed to link distributed brain dynamics to real-world action while recording behavior with high bandwidth [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Mobile brain imaging specifically enables simultaneous recording of EEG with body dynamics during active behavior, and this may support study designs with improved ecological validity [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Advances in lightweight systems and synchronized multimodal acquisition have further operationalized \u0026ldquo;natural cognition in action\u0026rdquo; approaches that integrate EEG with motion and physiological measures [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, movement-induced artifacts and other non-neural contamination typically increase with movement intensity, and methodological consensus on optimal mitigation strategies remains incomplete [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Foundational demonstrations show that artifact attenuation (e.g., template regression coupled with independent component analysis (ICA)) can recover meaningful EEG features during whole-body locomotion, supporting the broader feasibility of mobile EEG in dynamic tasks [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. At the same time, semi-periodic movement artifacts can sometimes produce components that resemble plausible brain sources, requiring careful validation when interpreting source-resolved results [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs concerning soccer-specific kicking studies, recent portable EEG experiments have begun to evaluate/explore the potential link between brain-derived signals and performance outcomes [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Despite this growth, mobile EEG in movement contexts still lacks broad consensus for quantifying and characterizing artifact burden [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In parallel movement domains, systematic evidence on EEG\u0026ndash;EMG connectivity shows substantial methodological variability and explicitly calls for better alignment of approaches, reinforcing the likelihood of similar harmonization needs in kicking research [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Alongside EEG, portable hemodynamic imaging such as near-infrared spectroscopy has long been positioned as a practical approach for studying cortical involvement during exercise and motor tasks in moving humans [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In movement science, functional near-infrared spectroscopy (fNIRS) applications have also been expanded, despite with heterogeneous protocols and data-processing choices which may limit comparability and interpretability across studies [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Consensus-oriented recommendations for fNIRS in posture and gait explicitly emphasize standardized conduct, artifact handling, and reporting, and such issues have been directly relevant when translating neuroimaging to complex sport skills [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. These developments indicate a timely need to consolidate what is known about brain-derived signals during soccer kicking and the technologies used to measure them, to better support performance science and rehabilitation translation [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. With these assumptions in mind, the current literature review aims to (i) systematically collate and critically evaluate the evidence on brain-derived signals reported in original research studies in relation to ball-kicking action in soccer and (ii) map the acquisition technologies and analytical pipelines used to capture and process these signals. We additionally aim to identify current methodological and translational gaps and derive evidence-informed directions for future neuro-mechanical research on soccer kicking.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003eThe protocol for the present review followed the items [Additional file 1] suggested by The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and was registered prior to the execution of the methodology processes (2025-06-09 04:03 PM GMT\u0026thinsp;\u0026minus;\u0026thinsp;5) in the OSF \u0026ndash; Open Science Framework under ID #nzasb (DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.17605/OSF.IO/NZASB\u003c/span\u003e\u003cspan address=\"10.17605/OSF.IO/NZASB\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), considering the structure according to a recently published systematic review [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. All the papers included in the current systematic review (i.e., qualitative synthesis of evidence) reported ethical aspects adopted for data collection in its full-texts and this was defined in as an inclusion criterion in the aforementioned protocol.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSearch strategy\u003c/h2\u003e \u003cp\u003eThe electronic databases IEEE Xplore, Scopus, Web of Science, APA PsycNet\u0026reg;, EBSCOHost, and PubMed were searched on 12/06/2025 (between 03:21 to 07:56 PM GMT\u0026thinsp;\u0026minus;\u0026thinsp;5) in an attempt to identify evidence about brain-derived signals related to ball kicking movement in soccer, published as articles within scientific journals. The search strategy and key terms were defined according to previous systematic reviews that analyzed on a separate basis the brain activity or ball kicking in the soccer context [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Using a Boolean search strategy, the final search string consisted of (soccer OR football* OR association football OR 11-a-side) AND (kick* OR shoot* OR pass* OR ball handling OR ball-kicking OR goal-directed OR skill OR technical) AND (brain OR cortex OR cortical OR neural OR neuronal OR EEG* OR electroencephalography OR fNIRS OR nirs OR functional near-infra*). The searches focused on the fields of title, abstract and keywords across all the aforementioned databases.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEligibility criteria\u003c/h3\u003e\n\u003cp\u003eThis review only considered original studies that were (i) scientific articles peer-reviewed; (ii) with abstract available for screening in the respective electronic database; (iii) full-text published in English language; using a PICOS/PECOS framework [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]: (iv) Participants \u0026ndash; when included human able-bodied subjects (e.g. soccer players) regardless of age; (v) Intervention/Exposure \u0026ndash; ball kick task; (vi) Comparator \u0026ndash; not applicable; (vii) Outcomes \u0026ndash; reported results of brain derived metrics (e.g. EEG power) and (viii) Study design \u0026ndash; no restrictions were imposed. No restrictions were also outlined in reference to the date of publication (i.e., articles were searched from inception up to the date of search completion reported here).\u003c/p\u003e\n\u003ch3\u003eStudy selection process\u003c/h3\u003e\n\u003cp\u003eInitially, the search results from all the databases were imported into an open-source reference management software (Zotero v.7.0.15; Corporation for Digital Scholarship, USA), which was used for the execution of this whole protocol. Thereafter the duplicates were removed automatically using its \u0026ldquo;Duplicate Items\u0026rdquo; function. Following on two authors (N.V. and F.Z.) independently evaluated the title, abstract and keywords of all registries identified in the electronic database search. Discrepancies were resolved in an additional consensus round with a third author, that consists also in a senior researcher in the area (L.V.). The evaluators were instructed to consider the eligibility criteria mentioned above and also to not consider inclusion of studies (i) published as book chapters, conference proceedings, or similar types (i.e. grey literature); (ii) if the article was later retracted; (iii) experiments conducted without reporting of any ethical aspects in the article text or the eventual exoneration \u0026ndash; if applicable [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]; (iv) considered task movements pertaining to football codes distinct to soccer; (v) with no information concerning the method/equipment adopted to record brain activity or (vi) with no information about which specific brain regions were considered for data collection.\u003c/p\u003e\n\u003ch3\u003eData extraction and evidence synthesis\u003c/h3\u003e\n\u003cp\u003eThe following information was extracted from included studies: publication details \u0026ndash; authors, year of publication and funding; demographic characteristics \u0026ndash; geographical location of the study, number of participants, sex, age, playing level and positional role; task protocol \u0026ndash; location, instruction/aim, ball characteristics, limb(s) used; data outcomes \u0026ndash; cortical areas evaluated, data collection device and acquisition frequency; signal processing techniques and pertinent findings. These items were selected taking into account previous systematic reviews [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. One author conducted the data extraction (M.F.) and a second author verified the extracted data (N.V.). In addition, to critically appraise the evidence collated, the STROBE checklist [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] was used to extract and provide a qualitative analysis (e.g. methods) for each of the of included studies. In particular, the Items 1, 3, 6, 8, 12, 14, 18, 19, 20 and 22 were selected for the present review. Two authors independently evaluated each study individually (N.V. and F.L.C.) and a third author (L.V.) checked the ratings and resolved any discrepancies if existed. Each item was rated using a numerical scale (\"completed\" = 1 or \"incomplete\" = 0). A sum of all items was then computed for each study. Based on that, the included studies were deemed to have low (\u0026sum; \u0026ge; 8) or high (\u0026sum; \u0026le; 7) risk of bias [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The robvis tool was used to generate the corresponding plot of methodological quality outcomes [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Finally, gap maps were also constructed to assist provide a synthesis of the findings across studies as well as potential directives for future investigations [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The threshold for judgement of a given study result as statistically significant was set at p\u0026thinsp;\u0026le;\u0026thinsp;0.05 unless otherwise stated.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 1748 reference entries were initially identified when pooling all results of the searches in the databases (IEEE Xplore – n = 148, Scopus – n = 573, Web of Science – n = 451, APA PsycNet® – n = 1, EBSCOHost – n = 408, and PubMed – n = 167). Next, 783 records were removed before screening due to automatic identification by the reference manager software as being duplicates (n = 759), published in a non-English language (n = 24) or retracted works (n = 4). Regarding the duplicates, the items deemed as the same indeed were merged and this process was supervised case-by-case. In the next stage, 236 reference entries were deleted owing to the fact that they were classified as grey literature, resulting in 729 records sought for retrieval. Following inspection of the titles, abstracts and keywords, 18 reports were assessed for eligibility, i.e. the step of full text analysis. After reading their full-texts, 8 [28–30, 50–54] were finally included in the review (Fig.\u0026nbsp;1). In the last screening stage, exclusions were due to (i) the use of a only virtual soccer task, (ii) only resting state measures reported, (iii) no brain outcomes reported, (iv) the use of only motor imagery paradigm, (v) paper published as conference proceeding, (vi) opinion piece article and (vii) covering other football codes.\u003c/p\u003e\n\u003cp\u003e****Insert Fig.\u0026nbsp;1 here****\u003c/p\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003eMain sample characteristics and purposes of studies\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;1 presents the sample characteristics and purpose of the included studies. Of the eight studies (with 183 participants), six included seniors, and one included youth participants, while one study [50] did not provide information on the age of the participants. Four studies (50%) involved only male participants [28, 29, 50, 53], three (38%) included both male and female participants [30, 51, 52], and in one study this information was not reported [54]. All studies included semi-professional or non-expert players; none included professional or elite players. Two studies reported the inclusion of samples from all playing positions [28, 29], while in the remaining studies (75%), the participants' positional roles were uncertain or unspecified. Sample sizes varied from 10 participants [29] to 39 participants [53]. One of the early studies, conducted by Collins et al. [50] in 1991, investigated changes in cerebral activity (i.e., alpha frequency) during a ball-kicking task among non-soccer-playing, physically active male participants. The participants were instructed to kick a soccer ball from a distance of 7 meters through a 30-centimeter-wide channel marked by two cones, while their cerebral activity was being recorded.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eSample characteristics and purposes of the included studies.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStudy purpose\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePlaying level\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePlaying position(s)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollins et al. [50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo investigate whether the changes in the alpha frequency band observed in experts during the karate modality also occurred in individuals relatively novice to the kicking task.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSub-elite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eunspecified\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLi et al. [29]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo investigate whether the changes in the 8–13 Hz frequency band are involved in successful and unsuccessful penalty kicks in skilled soccer players.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSenior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSub-elite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll (pooled)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePalucci Vieira et al. [28]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrazil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIdentify the magnitude of possible associations between the electroencephalographic signals and lower limb kinematic parameters during ball kicking action\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSub-elite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll (pooled)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePiskin et al. [51]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo investigate the cortical dynamics involved in target-directed kicking based on source-based analysis.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 female/8 male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSenior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSub-elite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eunspecified\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePiskin et al. [52]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo compare the kicking performance and associated cortical activity between injured and healthy soccer players.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 female/ 15 male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSenior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSub-elite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eunspecified\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePiskin et al. [30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo compare the neural mechanisms involved in passing between novice and experienced players.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 female/17 male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSenior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSub-elite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnspecified\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSchmaderer et al. [53]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo investigate the prefrontal activity of soccer experts during general and sport-specific cognitive tasks.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSenior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSub-elite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnspecified\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSlutter et al. [54]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNetherlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo compare brain activity during a penalty kick in which soccer players felt anxious and not anxious, using fNIRS.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSenior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSub-elite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003ePalucci et al. [28] in 2024 studied the association between brain oscillations at different frequency bands (i.e., delta, theta, alpha, beta, gamma) across different regions of the brain (i.e., frontal, motor, parietal, and occipital) during three phases of soccer kicking (i.e., preparation phase, approach phase, and immediately before ball contact phase) and ball velocity and mean radial error. The authors [28] involved 24 male U17 soccer players competing in regional-level competitions. In another study, Li et al. [29] in 2025 investigated EEG power in the 8–13 Hz frequency band at frontal and central regions before penalty kicks, comparing successful and unsuccessful attempts. The study [28] involved 10 right-footed male skilled soccer players who participated in regional-level tournaments in Germany.\u003c/p\u003e\n \u003cp\u003ePiskin et al. [51] in 2024 investigated cortical dynamics (measured using EEG) during soccer kicking, specifically aiming to pass the ball towards a target located three meters away. In another two studies, one also in 2024 and another in 2025, the authors used similar methodological procedures [27] and compared the cortical dynamics during soccer kicking between experienced and novice participants (Piskin et al. [30]) as well as between participants with an injury (i.e., anterior cruciate ligament-reconstructed players) and healthy individuals (Piskin et al. [52]).\u003c/p\u003e\n \u003cp\u003eSchmaderer et al. [53] in 2023 investigated the cortical processing of experienced soccer players during general and sport-specific tasks. The participants were involved in a sport-specific task requiring them to pass the ball against one of the three back-pass walls. A green square was the signal to pass amongst the three target walls. For the general cognition test, the participants were asked to stand in front of a 2 x 3 m wall and choose the accurate visual stimuli. A total of eight visual stimuli generators were present, and one color would change, and the participants had to move their hands in front of the stimulus. Slutter et al. [54] in 2021 compared brain activity during penalty kicks with a friendly goalkeeper, an amiable goalkeeper, and a competitive goalkeeper. In addition, the authors [54] compared the brain activity during these penalty kick conditions between experienced and inexperienced participants. The authors [54] also investigated the association between anxiety and brain activity during these penalty kick conditions.\u003c/p\u003e\n \u003cp\u003e****Insert Table\u0026nbsp;1 here****\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eOverview of data collection methods and technologies used\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;2 presents the data collection procedures and technologies used in the included studies. In this sense, two neuroimaging modalities were identified across studies: electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). The earliest study by Collins et al. [50] did not specify the brand of the EEG used to collect the data. Three studies [30, 51, 52] used EEG system from antiCap (Brain Products, Germany) while two studies used ANT Neuro (ANT Neuro b.v., Netherlands; Germany) [28, 29]. Regarding fNIRS, one study each utilized NIRx technologies (NIRx Medical Technologie, USA) [53] and Artinis Brite (Artinis Medical Systems, The Netherlands) [54]. The acquisition frequencies for EEG studies ranged from 250 Hz [50] to 1024 Hz [28] and most studies (4 out of 8 studies) used 500 Hz. The acquisition frequencies for fNIRS studies ranged from 8.7 Hz [53] to 10 Hz [54]. The experimental protocol used in the included studies varied, with only three studies [30, 51, 52] using similar protocols. Regarding the number of trials, these ranged from 6 trials [50] to 90 trials [30, 51, 52], with most studies using between 15 and 40 trials [28, 29, 53, 54]. Most studies evaluated the preferred limb, and only one study evaluated both the preferred and non-preferred limbs [50], while another study did not specify this methodological aspect [53]. The testing was conducted in laboratory conditions in three studies [30, 52, 53], and in outdoor conditions in two studies (artificial soccer pitch [54], and on natural grass pitch [28]), while three studies did not specify the environmental conditions [29, 50, 51].\u003c/p\u003e\n\u003cdiv\u003eCommon signal processing procedures in EEG studies included re-referencing/down-sampling, passband filtering, artifact suppression (by visual inspection and/or automatically with pre-defined thresholds), rejection of bad channels and rejection of components by independent component analysis (ICA). From the six studies assessing EEG, five [28–30, 51, 52] processed the data in the EEGLAB environment [55]. In this sense, Palucci Vieira et al. [28] used EEGLAB v2020.0 while Li et al. [29] do not specified the version of the toolbox used. The three studies by Piskin et al. [30, 51, 52] used the EEGLAB version 14.1.2b with identical processing methods across studies. As regarding fNIRS studies, processing procedures consisted generally in, as for example, passband filtering, automatic removal of data based on pre-defined thresholds and data transformation. Full details of the procedures for each study can be found in the Table 3. Schmaderer et al. [53] used the nirsLab software package v.2019.4, as did Slutter et al. [54] used OxySoft software. Complementary measurements generally consisted in motion kinematics: three studies [30, 51, 52] used wearable IMU systems (myoMOTION, Noraxon), one study used 3D videogrammetry [28] whilst another 2D [54]; both works employed GoPro® cameras for video recording.\u003c/div\u003e\n\u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBrain-derived data analysis and associated main outcomes in the included studies.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBrain channels collected\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrequency bands\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSignal processing (software and procedures)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePhase(s) of the movement analyzed\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBrain-derived outcomes computed\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdditional variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSummary\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollins et al. [50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT3, T2, C3, C4, P3, P4; Reference: Cz; Ground: spine (Thoracic 2).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ealpha: 8–13 Hz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCEAN 400 Acquisition System; Digitized amplified signal at a sampling rate of 256 Hz; Filters from 1 to 100 Hz; Frequency analysis by Fast Fourier Transform (FFT) in a spectrum from 1 to 60 Hz, using a cosine bell window with 256 points; alpha frequency band (8–13 Hz)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePreparatory (-2s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEEG-derived average power spectral density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSuccessful and unsuccessful performance of kick\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe authors identified that greater activity in the alpha frequency band before a kick can predict successful kicking performance in non-expert individuals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLi et al. [29]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFz and Cz; Reference: M1 and M2; Ground: AFz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ealpha: 8–13 Hz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEEGLAB: re-referenced to the averaged (m1, M2); bandpass (1–30 Hz); extracted epochs (-3000-1000ms); removed bad channels; rejected artifacts above ± 100 µV; ICA; interpolated channels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePreparatory (-2s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEEG- derived power through Welch estimation method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLearning effect: compare the success rate across the three blocks; Anxiety level: compare between and within subjects during the football penalty task\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSuccess in penalty kicks in a difficult task is characterized by lower power levels of 8 to 13 Hz in the frontal and central regions, indicating effective neuromotor allocation without compromising performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePalucci Vieira et al. [28]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF3, Fz, F4; C3, Cz, C4; P3, Pz, P4; O1, Oz, O2\u003c/p\u003e\n \u003cp\u003eReference: Common average\u003c/p\u003e\n \u003cp\u003eGround: --\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edelta: 0.5–3 Hz\u003c/p\u003e\n \u003cp\u003etheta: 4–7 Hz\u003c/p\u003e\n \u003cp\u003ealpha: 8–12 Hz\u003c/p\u003e\n \u003cp\u003ebeta: 13–30 Hz\u003c/p\u003e\n \u003cp\u003egamma: 31–50 Hz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEEGLAB v2020.0: Butterworth band-pass (0.3–50 Hz); notch filter (60 Hz); data re-sampled to 512 Hz; artifacts rejected by 1) visual inspection 2) channels with kurtosis \u0026gt; 3 standard deviations from the mean value, 3) epochs with absolute difference \u0026gt; 150 mV and 4) ICA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePreparatory (-6 to -3 s), approach (-3 to -1 s) and impact phase (-1 s to ball impact)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEEG-derived average power spectral density; ERS/ERD; ERSP and ITC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3D Kinematics: Angular joint (hip, knee and ankle) displacement and velocity; ROM; foot velocity; ball velocity; 2D kinematics: kicking accuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAuthors claim the association between kicking ball velocity and EEG-derived frontal theta power while kicking accuracy was related to occipital alpha power\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePiskin et al. [51]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClusters of brain sources, whose locations occurred in the parieto-occipital and mid-frontal regions; Reference: FCz; Ground: AFz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etheta (4–7 Hz); Alpha (8–10 Hz); Alpha-2 (11–13 Hz); Beta-1 (14-20Hz)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEEGLAB toolbox (version 14.1.2b): remove sinusoidal line noise; band-pass (3 -30Hz); artifacts rejected by 1) remove robust z-socre \u0026gt; 5; 2) Missing channels were interpolated; 3) data were then re- referenced to a common average and downsampled to 256 Hz; 4) an epoch time window of 3000 ms before and after kick-onset; 5) Baseline correction was performed from − 2500 to − 2000 ms; 6) ICA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePreparatory (-2s); Banckswing and swing: 0–1000 ms; Follow-through: 1000–2000 ms.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eERSP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3D biomechanics of the kick: reliability, movement range and peak acceleration; webcam: Accuracy rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe authors reveal that the right parieto-occipital cluster demonstrated strong alpha desynchronization after the kick, indicating increased visual demands. The mid-frontal cluster revealed theta synchronization before ball contact and alpha desynchronization beginning in the follow-through phase, indicating executive processing and attentional demands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePiskin et al. [52]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRight posterior and Mid-frontal Clusters (ERSP) AF3, AFz, AF4, F3, F1, Fz, F2 and F4; Pz, P2, P4, P6, P8, POz, PO8, Oz, O2 (MSE); Reference: FCz; Ground: AFz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etheta (4–7 Hz); Alpha (8–10 Hz); Alpha-2 (11–13 Hz); Beta-1 (14-20Hz)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEEGLAB toolbox (version 14.1.2b): remove sinusoidal line noise; band-pass (3 -30Hz); artifacts rejected by 1) remove robust z-score threshold \u0026gt; 5; 2) data were then re- referenced to a common average and downsampled to 256 Hz; 3) the data were epoched from − 3000 to 3000 ms, with baseline correction applied from − 2500 to -500 ms; 4) ICA; 5) All non-brain independent component were subsequently removed from the dataset\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrom − 3000 to 3000 ms, with baseline correction applied from − 2500 to -500 ms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eERSP and MSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3D biomechanics (hip flexion, knee flexion, foot external rotation and acceleration) were digitized and processed for complexity analysis in Matlab (MSE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe authors reveal that injured players exhibit sensorimotor alterations during kicking (differences in posterior alpha and frontal theta oscillations), suggesting compensatory strategies for performing the kicking task, hindering the integration of relevant information\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePiskin et al. [30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClusters of brain sources, whose locations occurred in the parieto-occipital and frontal regions; Reference: FCz; Ground: AFz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etheta (4–7 Hz); Alpha (8–10 Hz); Alpha-2 (11–13 Hz); Beta-1 (14-20Hz)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEEGLAB toolbox (version 14.1.2b): remove sinusoidal line noise; band-pass (3 -30Hz); artifacts rejected by 1) remove robust z-score threshold \u0026gt; 5; 2) data were then re- referenced to a common average and downsampled to 256 Hz; 3) the data was epoched to -0-2500 ms from pass onset to focus on post-onset; 4) ICA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost-onset (0–2500 ms) (ERSP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eERSP and MSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3D biomechanics (hip flexion, knee flexion, foot external rotation and acceleration) were digitized and processed for complexity analysis in Matlab (MSE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe authors revealed that more experienced players exhibit greater passing accuracy, which may indicate the influence of visuospatial and attentional strategies evidenced by alpha parieto-occipital desynchronization and theta frontal synchronization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSchmaderer et al. [53]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrontomedial: channel (ch) 11, 17–19; ventrolateral: ch 15–16, 20–21; dorsolateral: ch 1–10, 12–14.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enirsLab (2019.4): band pass filter (low cut-off frequency: 0.01 Hz; high cut-off frequency: 0.2 Hz); rejection of channels with variance \u0026gt; 7.5%; The first 45s were considered and divided into three time blooks 15s each; transformation to hemodynamic data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaseline (15s); After the start of the test (15 to 30s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage oxyhaemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReactivity and decision-making to visual and auditory stimuli; cognitive decision-making, sustained attention, reactivity, and decision-making in football situations, passes, and heart rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe authors revealed increased activity in the prefrontal region during novel stimuli compared to sport-specific tasks, a result of the automaticity acquired by experts in the field\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSlutter et al. [54]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMotor cortex: channel (ch): 1–4; right prefrontal cortex: ch 9–12; left prefrontal cortex: ch 9–12; left temporal cortex: ch 13–16; right and left dorsolateral prefrontal cortex: ch 17–18, respectively.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOxySoft - transform fNIRS signals; Scikit-learn package; Butterworth bandpass (0.02–0.5 Hz); Temporal derivative distribution repair method for artifact rejection; Tukey's biweight function; data were then re- referenced to baseline; rejection of channels with ultra-low FNIRS activity; the last 15 s of the resting period was used to subtract from datapoints; removal of data based on correlation coefficient (\u0026gt; 0.4) between O2Hb and HHb signals per trial-channel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrepatory (-5s) before the researcher's signal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO2Hb concentration; average O2Hb activation in each region; Averaged prefrontal cortex activation (left and right); prefrontal cortex asymmetry; connectivity index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall questionnaire: Sport Competition Anxiety Test; Sport Anxiety Scale; Successful and unsuccessful performance of kick; placement and shot power; goalkeeper-looking duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe study revealed distinct brain activation between anxious players (greater activation and asymmetry in the prefrontal cortex and less activation in the motor cortex) and non-anxious players, reinforcing the logic of the neural efficiency theory, which suggests greater activity in relevant areas and suppression of irrelevant ones\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: ERS/ERD = Event-related synchronisation/desynchronization; ERSP = Event-related spectral perturbation; O2Hb = chromophores oxygenated hemoglobin; HHb = deoxygenated hemoglobin; ITC = Inter-trial coherence; MSE = multiscale entropy; 3D = three-dimensional; 2D = two-dimensional; ICA = with independent component analysis. EEG = electroencephalogram; fNIRS = functional near-infrared spectroscopy. -- = information not reported or unclear.\u003c/p\u003e\n\u003cp\u003e****Insert Table\u0026nbsp;2 here****\u003c/p\u003e\n\u003ch3\u003eQualitative synthesis of evidence\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;3 presents the key outcomes derived from the included papers. Firstly, as regarding the EEG studies evaluating ball kicking [28–30, 50–52], four main dependent variables were computed across such investigations. These consisted of i) the event-related spectral perturbation (ERSP), being the most frequently reported outcome (4 of 6 studies; [28, 30, 51, 52]), followed by ii) EEG power (3 studies; [28, 29, 50]), iii) multiscale entropy (MSE; 2 studies; [30, 52]), and less frequently iv) the inter-trial coherence (ITC; 1 study [28]).\u003c/p\u003e\n\u003cp\u003eThe early study by Collins et al. [50] reported that in non-expert participants (i.e., non-soccer players), successful attempts (i.e., ball travelled through a 30-centimeter-wide channel) resulted in higher pre-kick alpha power at temporal sites than unsuccessful attempts. Palucci Vieira et al. [28] reported that the kicking velocity was associated with frontal theta power during the impact phase, while kicking accuracy was associated with occipital alpha power during the preparatory phase. In a similar study, Li et al. [29] reported that lower 8–13 Hz power at the frontal and central regions was associated with successful penalty kicks.\u003c/p\u003e\n\u003cp\u003ePiskin et al. [51] initially investigated the measurement error of the EEG to assess ball kicking and reported that the right parieto-occipital cluster demonstrated strong alpha desynchronization after kick, while the mid-frontal cluster revealed theta synchronization before ball contact and alpha desynchronization beginning in the follow-through phase. These variables were shown to have moderate to excellent reliability. Another study by Piskin et al. [30] reported that, compared to novice soccer players, expert soccer players showed greater passing accuracy and exhibited earlier and stronger alpha desynchronization at the right parieto-occipital region prior to ball contact, as well as stronger frontal theta synchronization at ball contact. In a third separate experiment evaluating the role of injuries on kicking-derived EEG signals, Piskin et al. [52] reported differences in cortical activation between participants with injuries and those without injuries; healthy participants exhibited stronger alpha desynchronization post-kick, whereas injured participants showed stronger theta synchronization at the mid-frontal cluster during both the onset of kick and the post-onset period. These findings suggest that injured athletes maintained accuracy by using compensatory strategies for the kicking task, which hindered the integration of relevant information.\u003c/p\u003e\n\u003cp\u003eFinally, regarding the evidence obtained in the studies [53, 54] using specifically the fNIRS technology to evaluate the ball kicking movement, Schmaderer et al. [53] reported that compared to known stimuli (i.e., sport-specific test), unknown or novel stimuli showed significantly higher prefrontal activity while Slutter et al. [54] reported differences in brain activation between anxious and non-anxious players, with anxious players exhibiting greater activation and asymmetry in the prefrontal cortex and lower activation in the motor cortex. The outcomes of the methodological quality assessment using the STROBE checklist is presented in Fig.\u0026nbsp;2. The overall risk of bias was reported to be low for all the included studies.\u003c/p\u003e\n\u003cp\u003e****Insert Table\u0026nbsp;3 here****\u003c/p\u003e\n\u003cp\u003e****Insert Fig.\u0026nbsp;2 here****\u003c/p\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eGap map\u003c/h2\u003e\n \u003cp\u003eFigure 3 illustrates the interaction between the brain parameters studied (frequency bands and cortical activation measures) and the brain regions explored in the included studies. In addition, Fig.\u0026nbsp;4 presents the gap mapping according to neuroimaging technologies, brain regions of interest, and key design features of the included studies.\u003c/p\u003e\n \u003cp\u003eThe alpha band oscillation was the most researched neural marker, including data in the frontal (n = 5 studies; [28–30, 51, 52]), parietal (n = 5 studies; [28, 30, 50–52]), occipital (n = 4 studies; [28, 30, 51, 52]), and central (n = 3 studies; [28, 29, 50]) brain regions. Limited data were found on alpha band activity in the temporal region (one study; [50]). Theta band oscillations were examined in the frontal, parietal and occipital (n = 4 studies for all the these three regions; [28, 30, 51, 52]), while available data on theta band were limited in the central region (one study; [28]). The beta frequency band (including the beta-1 sub-band, 14–20 Hz) was also investigated in the frontal, parietal, and occipital regions (n = 4 studies each; [28, 30, 51, 52]), and with limited evidence in the central region (one study; [28]). For delta and gamma frequency bands, only limited data were found, each examined in a single study [28] in the frontal, central, parietal, and occipital regions. Cortical oxygenation (fNIRS) was studied in the frontal cortex (two studies; [53, 54]) and to a limited extent in the motor cortex (one study; [54]), with no fNIRS data available for the parietal or occipital regions.\u003c/p\u003e\n \u003cp\u003eRegarding population characteristics and study methodology, results for experienced players during ball passing drills were the most researched (3 studies; [30, 51, 52]). Some data were also available for penalty kicks with experienced players (2 studies; [29, 53]), while limited data existed on target kicking with novice participants (1 study; [50]), and maximum velocity instep kicks with young players (1 study; [28]). No data were found (Fig.\u0026nbsp;4B) for exclusively female samples, professional/high-level or elite participants, samples of children or pre-adolescents, or for game-play conditions during brain signal acquisition. The majority of studies (6 out of 8) did not include any opponents [29, 30, 50–53], and only two studies included goalkeepers as opponents [28, 54]. On-field testing conditions were also scarce (2 studies; [28, 54]) while more studies were available using laboratory environments. Only limited evidence on the use of non-preferred limb was identified (1 study; [50]).\u003c/p\u003e\n \u003cp\u003eAs concerning the data acquisition technology, six studies employed EEG [28–30, 50–52], while two used fNIRS [53, 54] as their primary neuroimaging modality. No study combined both technologies simultaneously. Additional technologies were used to measure kinematics in five studies [28, 30, 51, 52, 54], while psychological measures (e.g., anxiety) were assessed in only one study [54]. Finally, only one study [51] formally evaluated the reliability of EEG signal measurements during the ball kicking task (Fig.\u0026nbsp;4C), as no equivalent reliability data are available for fNIRS-based protocols.\u003c/p\u003e\n \u003cp\u003e****Insert Fig.\u0026nbsp;3 here****\u003c/p\u003e\n \u003cp\u003e****Insert Fig.\u0026nbsp;4 here****\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis systematic review synthesized evidence on brain-derived signals measured during soccer ball-kicking tasks and related them to performance outcomes and contextual constraints. Convergent findings indicate that successful and/or higher-quality kicking performance is accompanied by phase-specific cortical dynamics, particularly within frontal (attentional/motor-programming), sensorimotor/central (motor control), and parieto-occipital (visuospatial) regions. In EEG studies, performance was most consistently linked to modulations in theta and alpha bands across distinct phases of the kick (preparation, approach/execution, follow-through), whereas fNIRS studies emphasized the role of prefrontal and motor-cortex oxygenation patterns under pressure/anxiety or varying cognitive demands. Using the gap map method, it was possible to observe that currently there are no literature studies using EEG\u0026ndash;fNIRS as part of the experimental paradigm. Evidence concerning data on measurement error (e.g., reliability) was also limited across studies. In the following paragraphs we will offer interpretations to the main findings of the present systematic review as well as possible directives for future investigations.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eNeurophysiological correlates of kicking success and performance outcomes\u003c/h2\u003e \u003cp\u003eIn penalty-kick contexts, successful trials were associated with lower 8\u0026ndash;13 Hz power in frontal and central regions during motor preparation (approximately the final 2 s pre-kick), suggesting a more efficient pre-action state and reduced costly cortical engagement for planning/control when execution is successful [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These results align with efficiency-oriented interpretations in which skilled performance is supported by selective recruitment of task-relevant networks without excessive prefrontal/sensorimotor activation that might reflect conscious control or maladaptive attentional capture [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEvidence also links brain signals to continuous performance dimensions (velocity and accuracy), rather than binary success alone. In youth sub-elite players performing instep shots from longer distance with a goalkeeper present, frontal theta activity was associated with ball velocity, whereas occipital alpha activity during preparation was associated with accuracy (mean radial error) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These associations map well onto a dual-demand structure of soccer kicking. Generating ball speed requires coordinated high-force multi-joint sequencing that may depend on frontal/cognitive control signals at critical instants, while accuracy depends heavily on visuospatial processing and stabilization of perceptual information (indexed here by posterior alpha modulation). Importantly, early evidence from a foundational study using a simpler task suggested that, in non-soccer participants, greater pre-kick alpha at temporal sites predicted successful execution [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. While that early result should be interpreted cautiously due to task simplicity and participant characteristics, it suggests that successful kicking can be preceded by measurable oscillatory differences even in relatively novice performers, albeit with topographies and interpretations that may differ from later mobile-neuroimaging paradigms.\u003c/p\u003e \u003cp\u003eOf note, one area underexplored in the studies evaluating the role of brain signals in the kinematics and outcomes of ball kicking refers to the asymmetries commonly observed. While there is extensive evidence as concerning the presence of asymmetry in the kinematics and outcomes of ball kicking, favoring the dominant side across investigations including various ages, genders and playing levels [\u003cspan additionalcitationids=\"CR57 CR58 CR59 CR60\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], the central mechanisms likely involved remain unclear. This is confirmed here since among the studies included in the present systematic review, only one considered kicks with the dominant and non-dominant limbs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e); in this isolated study authors provided a brief mention that there was no main effect for preferred vs. non-preferred kicking limb on brain-derived signals computed as well as it was not one of the objectives of the such study to evaluate asymmetries [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], thus implying existence of only limited evidence regarding this issue.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eVisuospatial and attentional strategies indexed by posterior alpha and frontal theta dynamics\u003c/h2\u003e \u003cp\u003eIn a reliability-focused EEG study using source-derived approaches, consistent parieto-occipital (alpha desynchronization) and mid-frontal (theta synchronization) dynamics were observed across sessions, supporting their candidacy as stable measure of directed pass-kicks [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Extending this, expertise comparisons showed that experienced players demonstrated higher pass accuracy alongside earlier/stronger parieto-occipital alpha desynchronization prior to ball contact and stronger frontal theta synchronization around ball contact [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. These findings are consistent with an expertise-related refinement of visuospatial attention (posterior alpha) and task-focused control/monitoring (frontal theta), potentially reflecting more efficient allocation of resources to extract target information and stabilize the sensorimotor plan.\u003c/p\u003e \u003cp\u003eComplementary evidence from fNIRS further supports the broader principle that familiarity/automaticity reduces prefrontal load. In a sample of semi-professional players, sport-specific (familiar) cognitive tasks elicited lower prefrontal activity changes than general (more novel) cognitive tasks, consistent with the interpretation that learned automatisms reduce reliance on effortful prefrontal processing [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Although this study assessed perceptual-cognitive tasks rather than solely biomechanics-focused kicking outcomes, it provides converging support suggesting that when task demands are familiar and well-trained, the system may achieve performance with lower prefrontal control signatures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePressure, anxiety, and injury as perturbations of neural efficiency during kicking\u003c/h2\u003e \u003cp\u003eIn an ecologically oriented penalty study using fNIRS, anxiety was associated with higher prefrontal activation and altered lateral asymmetry, alongside lower activation in the motor cortex; these patterns were linked to missed penalties and to anxious states, consistent with a choking-under-pressure interpretation emphasizing task-irrelevant prefrontal engagement [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Injury status (ACL reconstruction) similarly appears to be associated with altered cortical dynamics and movement variability during target-directed kicking. In the included injury comparison study, injured players exhibited distinct posterior alpha and frontal theta oscillatory patterns relative to healthy players, interpreted as compensatory attentional strategies that may support maintained accuracy at the expense of efficient visuospatial integration [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Possibly, soccer kicking can be understood not only as a biomechanical skill but also as a neurophysiological behavior that may be reorganized after injury, potentially contributing to persistent performance deficits or altered coordination strategies even when athletes have returned to play [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and future research\u003c/h2\u003e \u003cp\u003eCurrently, it is not uncommon to observe elite soccer players taking penalty kicks with a small jump just before contacting the ball [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], potentially aiming to delay their final movement and possibly make an online adjustment to the direction of the kick based on the goalkeeper's movements. This could in some ways contradict part of the literature that indicates that information about which side the goalkeeper decides to dive should be obtained by the penalty taker early in the approach run [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Among the studies included here, only one indicated that participating players could adopt an approach run with varying speeds (i.e. slow down) if they wanted to [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. However, to date there have been no studies that have analyzed brain signals specifically in relation to this contemporary type of kicking strategy using small jump/hop immediately before ball contact in shooting.\u003c/p\u003e \u003cp\u003eAnother important limitation identified in the present review study is that a goalkeeper attempting to block shots was used in only two of the included studies (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This might reduce the usefulness of the evidence collated here to \u0026ldquo;real-world\u0026rdquo; conditions. In fact, previous experimental studies indicate that unopposed testing scenarios have only limited value for predicting actual game performance in terms of ball kicking ability [\u003cspan additionalcitationids=\"CR65\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Notwithstanding, the role of vision has been also widely documented for having a direct impact on the outcomes of kicks in football [\u003cspan additionalcitationids=\"CR68\" citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Meanwhile, according to the present systematic review only half of the literature studies considered brain-derived measurements collected from the occipital region.\u003c/p\u003e \u003cp\u003eIn short, the principal limitation of the current evidence base is its relatively small size (eight original research articles published until the date of the searches) and high heterogeneity across tasks, contexts, technologies, and analytic methods, which precluded quantitative synthesis and limits the confidence of mechanistic claims. There is substantial diversity in tasks (short pass-kicks vs. long instep shots vs. penalties), environments (laboratory vs. artificial pitch vs. natural grass), and opponent presence, all of which likely modulate cortical demand profiles and complicate cross-study synthesis. One potential explanation for the small number of studies until the time of writing of this review is that the proposed experiments were probably only made possible by the most recent technological advances in the area (i.e. 88% of studies published after 2020 \u0026ndash; see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e); in fact, until shortly before that (in 2018), it was reported by Perrey and Besson [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e] an extreme difficulty in dealing with signal quality issues using both methodologies (EEG and fNIRS) in sports contexts. Future research should use more standardized task taxonomies with explicit manipulation of ecological constraints, preregister primary neurophysiological hypotheses to reduce analytic flexibility, expand beyond sub-elite samples and include women and youth players, and converge on harmonized reporting for mobile EEG/fNIRS in sport movements, including artifact quantification and sensitivity analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePractical applications\u003c/h2\u003e \u003cp\u003eThe emerging evidence suggests that training and rehabilitation programs may benefit from the cognitive\u0026ndash;sensorimotor processes indexed by frontal theta and posterior alpha dynamics. For performance training, coaches and practitioners could integrate practice designs that promote stable visuospatial attention and automated execution under accuracy constraints, while systematically introducing pressure elements to reduce maladaptive prefrontal over-engagement associated with anxiety and missed penalties [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. For injury rehabilitation, monitoring of task-related cortical dynamics and movement variability during standardized pass-kick tasks may help identify compensatory attentional strategies and guide progression toward more efficient visuospatial\u0026ndash;sensorimotor integration before full return to competition [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eEvidence from a small but growing literature indicates that soccer ball-kicking performance is associated with measurable, phase-specific cortical dynamics, most consistently involving frontal theta and parieto-occipital alpha modulations during preparation and execution. Across studies, better performance and expertise tend to align with patterns consistent with efficient attentional allocation and visuospatial processing, whereas anxiety and injury contexts appear to shift cortical engagement toward potentially compensatory, less efficient control strategies. While these findings are interesting, they remain preliminary due to limited study numbers, modest sample sizes, and heterogeneous methods. Given the complementary nature of temporal (EEG) and spatial (fNIRS) resolutions, the current systematic review with gap map identified that future research should attempt to evaluate ball kicking using both technologies concomitantly, as none of the studies included here has done this type of analysis until the moment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. All articles included in the current review reported respective ethical aspects adopted for data collection \u0026ndash; this was defined in the protocol as an inclusion criterion. The protocol of the review was registered in the OSF Preregistration - https://osf.io/nzasb/overview (DOI: https://doi.org/10.17605/OSF.IO/NZASB; Date created/registered: Jun 9, 2025).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLuiz H. Palucci Vieira: ongoing assignment received from PUCP - Tenure Track program. No author has any financial interest or received any financial benefit from this research. In addition, the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are thankful to the information made available by the Sistema de Bibliotecas-PUCP - https://biblioteca.pucp.edu.pe/ (accessed on 13 October 2025). We would also like to thank Fabio Barbieri for his suggestions in the early stage of the project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSimilarity and AI writing reports for the present paper can be found for download in https://doi.org/10.5281/zenodo.18716878. All raw data supporting this systematic review are derived from previously published studies, which have been cited in the text and reference list. Additional processed data that support the findings of the current review are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNU and LV designed the research. LV supervised the research activity planning and execution. NU and FZ conducted the literature search and screening steps. MF, FLC and NU conducted the data extraction and methodological quality evaluation, which were verified by LV. NU, FMC, RT, RKT, MF, FLC, FZ and LV interpreted the data analysis. FMC, RT, RKT and LV wrote the first draft of the manuscript with critical input from NU, MF, FLC and FZ. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eStafylidis A, Mandroukas A, Michailidis Y, Metaxas TI. Decoding Success: Predictive Analysis of UEFA Euro 2024 to Uncover Key Factors Influencing Soccer Match Outcomes. Appl Sci. 2024;14:7740. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/app14177740\u003c/span\u003e\u003cspan address=\"10.3390/app14177740\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKubayi A, Larkin P. Match-Related Statistics Differentiating Winning and Losing Teams at the 2019 Africa Cup of Nations Soccer Championship. Front Sports Act Living. 2022;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fspor.2022.807198\u003c/span\u003e\u003cspan address=\"10.3389/fspor.2022.807198\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVarley MC, Gregson W, McMillan K, Bonanno D, Stafford K, Modonutti M, et al. Physical and technical performance of elite youth soccer players during international tournaments: influence of playing position and team success and opponent quality. Sci Med Footb. 2017;1:18\u0026ndash;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/02640414.2016.1230676\u003c/span\u003e\u003cspan address=\"10.1080/02640414.2016.1230676\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOliva-Lozano JM, Yousefian F, Chmura P, Gabbett TJ, Cost R. Analysis of FIFA 2023 Women\u0026rsquo;s World Cup match performance according to match outcome and phase of the tournament. Biol Sport. 2024;42:71\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5114/biolsport.2025.142643\u003c/span\u003e\u003cspan address=\"10.5114/biolsport.2025.142643\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKubayi A, Larkin P. Technical performance of soccer teams according to match outcome at the 2019 FIFA Women\u0026rsquo;s World Cup. Int J Perform Anal Sport. 2020;20:908\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/24748668.2020.1809320\u003c/span\u003e\u003cspan address=\"10.1080/24748668.2020.1809320\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Jong LMS, Gastin PB, Angelova M, Bruce L, Dwyer DB. Technical determinants of success in professional women\u0026rsquo;s soccer: A wider range of variables reveals new insights. PLoS ONE. 2020;15:e0240992. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0240992\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0240992\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLees A, Asai T, Andersen TB, Nunome H, Sterzing T. The biomechanics of kicking in soccer: a review. J Sports Sci. 2010;28:805\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/02640414.2010.481305\u003c/span\u003e\u003cspan address=\"10.1080/02640414.2010.481305\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLees A, Nolan L. The biomechanics of soccer: a review. J Sports Sci. 1998;16:211\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/026404198366740\u003c/span\u003e\u003cspan address=\"10.1080/026404198366740\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKellis E, Katis A. Biomechanical characteristics and determinants of instep soccer kick. J Sports Sci Med. 2007;6:154\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShan G, Zhang X. From 2D leg kinematics to 3D full-body biomechanics-the past, present and future of scientific analysis of maximal instep kick in soccer. Sports Med Arthrosc Rehabil Ther Technol. 2011;3:23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1758-2555-3-23\u003c/span\u003e\u003cspan address=\"10.1186/1758-2555-3-23\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChow JW, Knudson DV. Use of deterministic models in sports and exercise biomechanics research. Sports Biomech. 2011;10:219\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/14763141.2011.592212\u003c/span\u003e\u003cspan address=\"10.1080/14763141.2011.592212\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalucci Vieira LH, Barbieri FA, Kellis E, Oliveira L, Aquino R, Cunha S, et al. Organisation of instep kicking in young U11 to U20 soccer players. Sci Med Footb. 2021;5:111\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/24733938.2020.1807043\u003c/span\u003e\u003cspan address=\"10.1080/24733938.2020.1807043\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Witt JK, Hinrichs RN. Mechanical factors associated with the development of high ball velocity during an instep soccer kick. Sports Biomech. 2012;11:382\u0026ndash;90. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/14763141.2012.661757\u003c/span\u003e\u003cspan address=\"10.1080/14763141.2012.661757\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalucci Vieira LH, Santinelli FB, Carling C, Kellis E, Santiago PRP, Barbieri FA. Acute Effects of Warm-Up, Exercise and Recovery-Related Strategies on Assessments of Soccer Kicking Performance: A Critical and Systematic Review. Sports Med. 2021;51:661\u0026ndash;705. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40279-020-01391-9\u003c/span\u003e\u003cspan address=\"10.1007/s40279-020-01391-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalucci Vieira LH. Holistic approach to testing ball kicking mechanics and outcome metrics in soccer: Methodological aspects, observation and intervention (PhD Academy Award). Br J Sports Med. 2024;58:345\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerrey S, Besson P. Studying brain activity in sports performance: Contributions and issues. Prog Brain Res. 2018;240:247\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/bs.pbr.2018.07.004\u003c/span\u003e\u003cspan address=\"10.1016/bs.pbr.2018.07.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanes JN, Donoghue JP. Plasticity and primary motor cortex. Annu Rev Neurosci. 2000;23:393\u0026ndash;415. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev.neuro.23.1.393\u003c/span\u003e\u003cspan address=\"10.1146/annurev.neuro.23.1.393\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarni A, Meyer G, Rey-Hipolito C, Jezzard P, Adams MM, Turner R, et al. The acquisition of skilled motor performance: fast and slow experience-driven changes in primary motor cortex. Proc Natl Acad Sci U S A. 1998;95:861\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.95.3.861\u003c/span\u003e\u003cspan address=\"10.1073/pnas.95.3.861\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShadmehr R, Smith MA, Krakauer JW. Error correction, sensory prediction, and adaptation in motor control. Annu Rev Neurosci. 2010;33:89\u0026ndash;108. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev-neuro-060909-153135\u003c/span\u003e\u003cspan address=\"10.1146/annurev-neuro-060909-153135\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShibasaki H, Hallett M. What is the Bereitschaftspotential? Clin Neurophysiol. 2006;117:2341\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.clinph.2006.04.025\u003c/span\u003e\u003cspan address=\"10.1016/j.clinph.2006.04.025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeuper C, W\u0026ouml;rtz M, Pfurtscheller G. ERD/ERS patterns reflecting sensorimotor activation and deactivation. Prog Brain Res. 2006;159:211\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0079-6123(06)59014-4\u003c/span\u003e\u003cspan address=\"10.1016/S0079-6123(06)59014-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePfurtscheller G, Neuper C, Andrew C, Edlinger G. Foot and hand area mu rhythms. Int J Psychophysiol. 1997;26:121\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s0167-8760(97)00760-5\u003c/span\u003e\u003cspan address=\"10.1016/s0167-8760(97)00760-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMakeig S, Gramann K, Jung T-P, Sejnowski TJ, Poizner H. Linking brain, mind and behavior. Int J Psychophysiol. 2009;73:95\u0026ndash;100. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijpsycho.2008.11.008\u003c/span\u003e\u003cspan address=\"10.1016/j.ijpsycho.2008.11.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGramann K, Ferris DP, Gwin J, Makeig S. Imaging natural cognition in action. Int J Psychophysiol. 2014;91:22\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijpsycho.2013.09.003\u003c/span\u003e\u003cspan address=\"10.1016/j.ijpsycho.2013.09.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGorjan D, Gramann K, De Pauw K, Marusic U. Removal of movement-induced EEG artifacts: current state of the art and guidelines. J Neural Eng. 2022;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/1741-2552/ac542c\u003c/span\u003e\u003cspan address=\"10.1088/1741-2552/ac542c\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGwin JT, Gramann K, Makeig S, Ferris DP. Removal of movement artifact from high-density EEG recorded during walking and running. J Neurophysiol. 2010;103:3526\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1152/jn.00105.2010\u003c/span\u003e\u003cspan address=\"10.1152/jn.00105.2010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnyder KL, Kline JE, Huang HJ, Ferris DP. Independent Component Analysis of Gait-Related Movement Artifact Recorded using EEG Electrodes during Treadmill Walking. Front Hum Neurosci. 2015;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnhum.2015.00639\u003c/span\u003e\u003cspan address=\"10.3389/fnhum.2015.00639\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalucci Vieira LH, Carling C, da Silva JP, Santinelli FB, Polastri PF, Santiago PRP, et al. Modelling the relationships between EEG signals, movement kinematics and outcome in soccer kicking. Cogn Neurodyn. 2022;16:1303\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11571-022-09786-2\u003c/span\u003e\u003cspan address=\"10.1007/s11571-022-09786-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi D, Elbanna H, Lin F-Y, Lu C-J, Chen L-J, Lu G, et al. Neuromotor mechanisms of successful football penalty kicking: an EEG pilot study. Front Psychol. 2025;16:1452443. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpsyg.2025.1452443\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2025.1452443\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePiskin D, M\u0026uuml;ller R, B\u0026uuml;chel D, Lehmann T, Baumeister J. Behavioral and cortical dynamics underlying superior accuracy in short-distance passes. Behav Brain Res. 2024;471:115120. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bbr.2024.115120\u003c/span\u003e\u003cspan address=\"10.1016/j.bbr.2024.115120\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJacobsen NSJ, Blum S, Witt K, Debener S. A walk in the park? Characterizing gait-related artifacts in mobile EEG recordings. Eur J Neurosci. 2021;54:8421\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ejn.14965\u003c/span\u003e\u003cspan address=\"10.1111/ejn.14965\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeynaeve M, Mantini D, de Beukelaar TT. Electrophysiological Approaches to Understanding Brain-Muscle Interactions During Gait: A Systematic Review. Bioeng (Basel). 2025;12:471. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/bioengineering12050471\u003c/span\u003e\u003cspan address=\"10.3390/bioengineering12050471\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerrey S. Non-invasive NIR spectroscopy of human brain function during exercise. Methods. 2008;45:289\u0026ndash;99. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ymeth.2008.04.005\u003c/span\u003e\u003cspan address=\"10.1016/j.ymeth.2008.04.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerold F, Wiegel P, Scholkmann F, Thiers A, Hamacher D, Schega L. Functional near-infrared spectroscopy in movement science: a systematic review on cortical activity in postural and walking tasks. Neurophotonics. 2017;4:041403. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1117/1.NPh.4.4.041403\u003c/span\u003e\u003cspan address=\"10.1117/1.NPh.4.4.041403\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMenant JC, Maidan I, Alcock L, Al-Yahya E, Cerasa A, Clark DJ, et al. A consensus guide to using functional near-infrared spectroscopy in posture and gait research. Gait Posture. 2020;82:254\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gaitpost.2020.09.012\u003c/span\u003e\u003cspan address=\"10.1016/j.gaitpost.2020.09.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBirbaumer N, Weber C, Neuper C, Buch E, Haapen K, Cohen L. Physiological regulation of thinking: brain-computer interface (BCI) research. Prog Brain Res. 2006;159:369\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0079-6123(06)59024-7\u003c/span\u003e\u003cspan address=\"10.1016/S0079-6123(06)59024-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePage MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst Reviews. 2021;10:89. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13643-021-01626-4\u003c/span\u003e\u003cspan address=\"10.1186/s13643-021-01626-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarmento H, Afonso J, Clemente F, Gouveia \u0026Eacute;R, Ordo\u0026ntilde;ez-Saavedra N, Silva J, et al. Unlocking the power of set pieces in men\u0026rsquo;s professional football - a scoping review. Int J Sports Med. 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1055/a-2563-0323\u003c/span\u003e\u003cspan address=\"10.1055/a-2563-0323\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamacher D, Herold F, Wiegel P, Hamacher D, Schega L. Brain activity during walking: A systematic review. Neurosci Biobehavioral Reviews. 2015;57:310\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neubiorev.2015.08.002\u003c/span\u003e\u003cspan address=\"10.1016/j.neubiorev.2015.08.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalucci Vieira LH, Clemente FM, Silva RM, Vargas-Villafuerte KR, Carpes FP. Measurement Properties of Wearable Kinematic-Based Data Collection Systems to Evaluate Ball Kicking in Soccer: A Systematic Review with Evidence Gap Map. Sens (Basel). 2024;24:7912. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/s24247912\u003c/span\u003e\u003cspan address=\"10.3390/s24247912\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHiggins J, Thomas J, Chandler J, Cumpston M, Li T, Page M, et al. Cochrane Handbook for Systematic Reviews of Interventions. Online: The Cochrane Collaboration; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWinter EM, Maughan RJ. Requirements for ethics approvals. J Sports Sci. 2009;27:985\u0026ndash;985. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/02640410903178344\u003c/span\u003e\u003cspan address=\"10.1080/02640410903178344\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVergnes J-N, Marchal-Sixou C, Nabet C, Maret D, Hamel O. Ethics in systematic reviews. J Med Ethics. 2010;36:771\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/jme.2010.039941\u003c/span\u003e\u003cspan address=\"10.1136/jme.2010.039941\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVitorio R, Stuart S, Rochester L, Alcock L, Pantall A. fNIRS response during walking - Artefact or cortical activity? A systematic review. Neurosci Biobehav Rev. 2017;83:160\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neubiorev.2017.10.002\u003c/span\u003e\u003cspan address=\"10.1016/j.neubiorev.2017.10.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evon Elm E, Altman DG, Egger M, Pocock SJ, G\u0026oslash;tzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370:1453\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(07)61602-X\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(07)61602-X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO\u0026rsquo;Reilly M, Caulfield B, Ward T, Johnston W, Doherty C. Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review. Sports Med. 2018;48:1221\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40279-018-0878-4\u003c/span\u003e\u003cspan address=\"10.1007/s40279-018-0878-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRico-Gonz\u0026aacute;lez M, Pino-Ortega J, M\u0026eacute;ndez A, Clemente FM, Baca A. Machine learning application in soccer: a systematic review. Biol Sport. 2023;40:249\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5114/biolsport.2023.112970\u003c/span\u003e\u003cspan address=\"10.5114/biolsport.2023.112970\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcGuinness LA, Higgins JPT. Risk-of-bias VISualization (robvis): An R package and Shiny web app for visualizing risk-of-bias assessments. Res Synthesis Methods. 2021;12:55\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jrsm.1411\u003c/span\u003e\u003cspan address=\"10.1002/jrsm.1411\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalucci Vieira LH, Clemente FM, Chang Marquez FA, Rea Olivares WM, Vargas Villafuerte KR, Carpes FP. Accuracy Standards of Wearable Technologies for Assessment of Soccer Kicking: Protocol for a Systematic Literature Review. JMIR Res Protoc. 2024;13:e57433. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2196/57433\u003c/span\u003e\u003cspan address=\"10.2196/57433\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollins D, Powell G, Davies I. Cerebral activity prior to motion task performance: an electroencephalographic study. J Sports Sci. 1991;9:313\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/02640419108729892\u003c/span\u003e\u003cspan address=\"10.1080/02640419108729892\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePiskin D, B\u0026uuml;chel D, Lehmann T, Baumeister J. Reliable electrocortical dynamics of target-directed pass-kicks. Cogn Neurodyn. 2024;18:2343\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11571-024-10094-0\u003c/span\u003e\u003cspan address=\"10.1007/s11571-024-10094-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePiskin D, Cobani G, Lehmann T, B\u0026uuml;chel D, Baumeister J. Cortical changes associated with an anterior cruciate ligament injury may retrograde skilled kicking in football: preliminary EEG findings. Sci Rep. 2025;15:2208. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-025-86196-4\u003c/span\u003e\u003cspan address=\"10.1038/s41598-025-86196-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmaderer LF, Meyer M, Reer R, Schumacher N. What happens in the prefrontal cortex? Cognitive processing of novel and familiar stimuli in soccer: An exploratory fNIRS study. Eur J Sport Sci. 2023;23:2389\u0026ndash;99. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/17461391.2023.2238699\u003c/span\u003e\u003cspan address=\"10.1080/17461391.2023.2238699\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlutter MWJ, Thammasan N, Poel M. Exploring the Brain Activity Related to Missing Penalty Kicks: An fNIRS Study. Frontier Comput Sci. 2021;3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcomp.2021.661466\u003c/span\u003e\u003cspan address=\"10.3389/fcomp.2021.661466\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods. 2004;134:9\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jneumeth.2003.10.009\u003c/span\u003e\u003cspan address=\"10.1016/j.jneumeth.2003.10.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHunter AH, Smith NMA, Camata TV, Crowther MS, Mather A, Souza NM, et al. Age- and size-corrected kicking speed and accuracy in elite junior soccer players. Sci Med Footb. 2022;6:29\u0026ndash;39. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/24733938.2021.1899274\u003c/span\u003e\u003cspan address=\"10.1080/24733938.2021.1899274\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeixeira MCT, Teixeira LA. Leg preference and interlateral performance asymmetry in soccer player children. Dev Psychobiol. 2008;50:799\u0026ndash;806. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/dev.20322\u003c/span\u003e\u003cspan address=\"10.1002/dev.20322\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarlsson T, Isberg J, Nilsson J, Carlsson M. The Influence of Task Conditions on Side Foot-Kick Accuracy among Swedish First League Women\u0026rsquo;s Soccer Players. J Sports Sci Med. 2018;17:74\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarfield WR, Kirkendall DT, Yu B. Kinematic instep kicking differences between elite female and male soccer players. J Sports Sci Med. 2002;1:72\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOutram T, Freeman H, Briley S. The effect of leg dominance on the frequency and 3D kinematics of soccer passing in female academy players. Paris, France: European College of Sports Science; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVieira LHP, de Souza Serenza F, de Andrade VL, de Paula Oliveira L, Mariano FP, Santana JE, et al. Kicking Performance and Muscular Strength Parameters with Dominant and Nondominant Lower Limbs in Brazilian Elite Professional Futsal Players. J Appl Biomech. 2016;32:578\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1123/jab.2016-0125\u003c/span\u003e\u003cspan address=\"10.1123/jab.2016-0125\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng R, van der Kamp J, Miller-Dicks M, Navia J, Savelsbergh G. The effectiveness of penalty takers\u0026rsquo; deception: A scoping review. Hum Mov Sci. 2023;90:103122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.humov.2023.103122\u003c/span\u003e\u003cspan address=\"10.1016/j.humov.2023.103122\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNavarro M, van der Kamp J, Ranvaud R, Savelsbergh GJP. The mere presence of a goalkeeper affects the accuracy of penalty kicks. J Sports Sci. 2013;31:921\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/02640414.2012.762602\u003c/span\u003e\u003cspan address=\"10.1080/02640414.2012.762602\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSerpiello FR, Cox A, Oppici L, Hopkins WG, Varley MC. The Loughborough Soccer Passing Test has impractical criterion validity in elite youth football. Sci Med Footb. 2017;1:60\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/02640414.2016.1254810\u003c/span\u003e\u003cspan address=\"10.1080/02640414.2016.1254810\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR\u0026eacute; AHN, Cattuzzo TM, Santos FMC, Monteiro CBM. Anthropometric characteristics, field test scores and match-related technical performance in youth indoor soccer players with different playing status. Int J Perform Anal Sport. 2014;14:482\u0026ndash;92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/24748668.2014.11868737\u003c/span\u003e\u003cspan address=\"10.1080/24748668.2014.11868737\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrth D, Davids K, Ara\u0026uacute;jo D, Renshaw I, Passos P. Effects of a defender on run-up velocity and ball speed when crossing a football. Eur J Sport Sci. 2014;14(Suppl 1):S316\u0026ndash;323. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/17461391.2012.696712\u003c/span\u003e\u003cspan address=\"10.1080/17461391.2012.696712\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagano T, Kato T, Fukuda T. Visual Behaviors of Soccer Players While Kicking with the inside of the Foot. Percept Mot Skills. 2006;102:147\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2466/pms.102.1.147-156\u003c/span\u003e\u003cspan address=\"10.2466/pms.102.1.147-156\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoel B, Van Der Kamp J. Gaze behaviour during the soccer penalty kick: an investigation of the effects of strategy and anxiety. Int J Sport Psycol. 2012;43:326.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePiras A, Vickers JN. The effect of fixation transitions on quiet eye duration and performance in the soccer penalty kick: instep versus inside kicks. Cogn Process. 2011;12:245\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10339-011-0406-z\u003c/span\u003e\u003cspan address=\"10.1007/s10339-011-0406-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerrey S, Besson P. Studying brain activity in sports performance: Contributions and issues. Prog Brain Res. 2018;240:247\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/bs.pbr.2018.07.004\u003c/span\u003e\u003cspan address=\"10.1016/bs.pbr.2018.07.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 2","content":"\u003cp\u003eTable 2 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"measurement, portable neuroimaging systems, EEG, fNIRS, team sports, biomechanics","lastPublishedDoi":"10.21203/rs.3.rs-8940911/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8940911/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRecent technological advances have enabled the development of portable data acquisition systems that facilitate the collection of brain signals during sports tasks. The main objective of the present systematic review was to collate evidence regarding studies that have analysed brain-derived indices related to ball kicking action.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e The PRISMA guidelines were followed in the previously established protocol for this review. Six electronic databases were used for searches (IEEE Xplore, Scopus, Web of Science, APA PsycNet, EBSCOHost, and PubMed). The search string was formulated based on the following PICOS/PECOS framework: participants as human able-bodied subjects regardless of age, evaluated while performing a ball kick task, and reported results of brain-derived metrics. The STROBE checklist was used to evaluate the methodological quality of the included studies.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe database searches resulted in a total of 1748 records, of which 8 original research articles met all the inclusion criteria. Most studies used EEG systems while few employed fNIRS. Qualitative synthesis indicated that skilled ball kicking performance was accompanied by phase-specific cortical dynamics (e.g. within frontal, sensorimotor/central, and parieto-occipital regions).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eBetter outcomes tended to be linked with brain patterns related to efficient attentional allocation and visuospatial processing, whereas anxiety and injury appear to shift cortical engagement toward potentially compensatory, less efficient control strategies. Finally, one problem identified in this review was that only 25% literature studies used an opponent attempting to block the shots. Future studies need to improve the design of experimental tasks so that they more closely resemble what occurs in a real game.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003eThe review protocol was registered in OSF Preregistration under ID #NZASB.\u003c/p\u003e","manuscriptTitle":"Brain-derived Signals Related to Ball Kicking Movement in Soccer and Technologies Employed: A Systematic Literature Review With Gap Map","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-13 07:37:34","doi":"10.21203/rs.3.rs-8940911/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-12T10:13:39+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"104702477705253657562333234222653679600","date":"2026-03-11T17:51:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-11T14:15:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-10T21:49:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"277110082031799680127396360601044328191","date":"2026-03-10T16:52:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265608092584404510981021198294610877706","date":"2026-03-09T09:50:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121266580762153135086246811128371723215","date":"2026-03-08T17:35:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246721282496416211676265931461348185883","date":"2026-03-08T17:28:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"212957007712305079316381597398101852410","date":"2026-03-08T17:16:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"322076014384596392823171111949931511011","date":"2026-03-06T17:17:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235959287606585940970303036896104831166","date":"2026-03-06T17:16:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-06T17:14:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-05T05:25:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-04T12:16:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Sports Science, Medicine and Rehabilitation","date":"2026-02-26T21:00:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eebaddd9-2ea7-46e5-af1c-5e5a6f181451","owner":[],"postedDate":"March 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:10:53+00:00","versionOfRecord":{"articleIdentity":"rs-8940911","link":"https://doi.org/10.1186/s13102-026-01676-y","journal":{"identity":"bmc-sports-science-medicine-and-rehabilitation","isVorOnly":false,"title":"BMC Sports Science, Medicine and Rehabilitation"},"publishedOn":"2026-04-01 15:58:02","publishedOnDateReadable":"April 1st, 2026"},"versionCreatedAt":"2026-03-13 07:37:34","video":"","vorDoi":"10.1186/s13102-026-01676-y","vorDoiUrl":"https://doi.org/10.1186/s13102-026-01676-y","workflowStages":[]},"version":"v1","identity":"rs-8940911","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8940911","identity":"rs-8940911","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}