Violin Major Music Alleviates University Students’ Anxiety Maybe through the dACC and DLPFC Circuits

Violin Major Music Alleviates University Students’ Anxiety Maybe through the dACC and DLPFC Circuits | 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 Article Violin Major Music Alleviates University Students’ Anxiety Maybe through the dACC and DLPFC Circuits Qianwen LUO, Yifan WANG, Kairui YANG, Airong QIAN, Pei NIE, Min XI, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6879305/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract University students may encounter high levels of anxiety or depression, which can adversely impact their daily lives and academic performance and even lead to illness. Recently, studies have reported that music therapy can effectively alleviate anxiety, although the underlying mechanisms remain unclear. In this study, we assessed therapeutic outcomes and explored several cognitive mechanisms of "major keys", "minor keys", "violin music" and "piano music" for individuals experiencing university-related anxiety. Our assessments included power spectral density values of beta and gamma brain waves, as well as quantitative evaluations of musical pieces and brain localization experiences. The results indicated that anxious participants exposed to major key piano music experienced a significant reduction in anxiety levels compared with those in the minor key piano music group. The brain localization results further suggested that gamma waves are more suitable than beta waves for highly anxious participants. The possible neural circuits involved may include the activation of the dorsomedial prefrontal cortex (dACC) and the left dorsolateral prefrontal cortex (DLPFC). Our findings revealed that violin music in the major key can effectively relieve anxiety through the activation of the dACC and DLPFC circuits. Biological sciences/Neuroscience/Emotion/Prefrontal cortex Biological sciences/Neuroscience/Neural circuit Biological sciences/Psychology/Human behaviour Anxiety Music therapy Violin Major key EEG sLORETA dACC DLPFC Gamma waves Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Background State anxiety is a prevalent emotion that, if left unmanaged, can escalate into anxiety disorders, potentially leading to severe health issues such as migraines, cardiovascular diseases, and even cancer (Szuhany & Simon, 2022 ). The economic impact of anxiety is substantial, with annual healthcare costs and productivity losses estimated to surpass $ 4 billion (Harder et al., 2016 ). Therefore, early intervention for individuals experiencing anxiety is imperative to prevent its progression into a disorder. University students, with their heightened awareness of personal, interpersonal, and sociocultural differences, coupled with their openness to change, are particularly susceptible to psychological issues that can significantly impair cognitive functions such as thinking, perception, and learning (Endler & Kocovski, 2001 ; Kocsis, 2013 ). Addressing anxiety among university students is thus crucial for their academic success and overall well-being. Existing treatments for anxiety include pharmacotherapy (PT) and cognitive‒behavioral therapy (CBT). However, certain anxiolytic drugs increase the risk of dementia, psychomotor disorders, pneumonia, and cancer (Weich et al., 2014 ). Moreover, traditional CBT has limitations, with up to 36% of anxiety disorder patients not responding to it and as many as 40% of children and adolescents with anxiety disorders experiencing relapse after discontinuing CBT (Ginsburg et al., 2014 ; Swain et al., 2013 ). Therefore, exploring new and safe methods for alleviating anxiety is essential. Recently, music therapy has emerged as a noninvasive treatment option with minimal side effects and a broad target population. Multiple studies have confirmed the clinical potential of music interventions for state anxiety. These studies demonstrate that music therapy can significantly relieve social anxiety disorders, depressive symptoms, and pain (Tang et al., 2021 ; Zhu et al., 2023 ). Collectively, these findings suggest that music therapy has extensive potential applications in various clinical settings, warranting further promotion and implementation. However, recent studies lack sufficient focus on younger populations, particularly university students, and inadequately address the individual characteristics of music (Lu et al., 2021 ). Additionally, the analysis of music's physical properties and the mechanisms of neural circuits remains limited (Nilsson, 2008 ). A wide range of musical features have been studied in various research contexts. Bach's classical music is often used because of its consistent style and therapeutic effectiveness (Sharda et al., 2019 ). Tonality plays a pivotal role in conveying emotions, with major and minor modes typically associated with feelings of happiness and sadness, respectively, due to their characteristic intervals (Balkwill & Thompson, 1999 ; Nieminen et al., 2012 ). The distinct timbres of the piano and violin also contribute to their differential effects on anxiety. The piano offers a balanced and wide harmonic spectrum, whereas the violin produces rich higher harmonics through bow friction (Sethares, 2005 ). On the basis of these findings, we hypothesize that a specific musical element may be more effective in influencing anxiety states. Neurological research has confirmed that changes in the power density values of gamma and beta waves can serve as useful indicators of anxiety levels. Neuronal activity in the gamma frequency band increases when emotional and threat-related stimuli are processed (Keil et al., 2001 ; Müller et al., 1999 ; Oya et al., 2002 ). Furthermore, beta wave activity is closely related to emotional states such as anxiety, and a decrease in beta wave activity can reflect anxiety relief (Davidson, 2000 ). The prefrontal electrodes (F3, F4) are widely used in studies on emotion regulation and anxiety-related brain activity (Vanhollebeke et al., 2022 ), while the central electrodes (C3, C4) have been employed in research on emotional regulation and cognitive function (Yang et al., 2015 ). These electrodes F3, F4, C3, and C4 also exhibit significant differences in beta and gamma power and have been used to investigate EEG spectral changes associated with anxiety and depression (Chen et al., 2025 ). Therefore, we consider the reduction in gamma and beta wave power spectral density on electrodes F3, F4, C3, C4 as an indicator of anxiety alleviation. The dorsal anterior cingulate cortex (dACC) and the dorsolateral prefrontal cortex (DLPFC) around these electrodes play significant roles in emotional regulation associated with fear and anxiety, involving the modulation of emotional conflict and the extinction of fear memories (Etkin et al., 2011 ; Fox et al., 2012 ). Therefore, we speculate that these brain regions may also be related to the neurological principles underlying music therapy. The current study aimed to investigate the impact of different musical instruments (piano and violin) and tonalities (major and minor) on anxiety relief in university students. EEG data of beta and gamma waves from the Prefrontal Cortex, which is channel F3, F4, C3 and C4, were collected for data analysis, with a music database being selected on the basis of Bach Werke Verzeichnis (BWV). The EEG data were further analyzed via wavelet transform and sLORETA brain localization. Moreover, the musical pieces underwent a spectrum analysis in terms of frequency density value and roughness. Our findings offer a new perspective on methods for alleviating anxiety and enhance our understanding of music therapy. 2. Methods and materials 2.1 Participants All participants provided written informed consent, which was approved by the Ethics Committee of Biology and Medicine at Northwestern Polytechnical University in China. All methods were performed in accordance with the relevant guidelines and regulations. The inclusion criteria for participants were recent experience of anxiety, absence of any other psychiatric disorders or familial psychiatric history, and no prior professional musical training. A total of 74 undergraduate and graduate students from Northwestern Polytechnical University in China were recruited and divided into two groups on the basis of their Self-Rating Anxiety Scale (SAS) score (Zung, 1971 ): an anxiety group (comprising 26 females and 12 males, with a mean age of 24.47 years and a standard deviation of 3.86 years) and a normal control group (consisting of 18 females and 12 males, with a mean age of 23.36 years and a standard deviation of 1.79 years) (Table 1 ). All the participants were subsequently randomly assigned to one of four groups (PA, PI, VA, VI), where "P" represents the piano group, "V" signifies the violin group, "A" denotes the major key, and "I" indicates the minor key, to receive various musical interventions. Table 1 Demographic data of participants recruited for the anxiety group and normal control group. Anxiety group (n = 38) Normal control (n = 30) Age 24.47 (3.86) 23.36 (1.79) Female gender 26 18 Male gender 12 12 SAS scores 57.10 (8.54) 36.4 (5.40) 2.2 Stimuli The musical intervention materials utilized in the experiment included meticulously selected and edited instrumental music excerpts sourced from Bach's Works (BWV). As illustrated in Fig. 1 A, the entire selection and processing procedure encompassed six distinct stages: the establishment of a comprehensive music database, the categorization of musical materials, selection on the basis of tempo, the editing of segments, splicing and transitions, and final completion. Initially, pieces from Bach's keyboard works (BWV 772–994) and chamber music compositions (BWV 1001–1040) were chosen and compiled into a primary music database. The pieces in this database were subsequently categorized by instrument and tonality into four distinct groups (PA, PI, VA, VI), thereby forming a secondary database. From this secondary database, pieces with tempos ranging between 60 and 120 beats per minute (BPM) were selected, with five pieces from each group being chosen to establish a tertiary database. During the segment editing phase, each selected piece underwent clipping to extract a coherent musical phrase, ideally spanning approximately 1–2 minutes and encompassing a cadence. Following this, within each group, various musical segments were randomly spliced together, with the addition of transition effects at the commencement and conclusion of each track to create a cohesive and smooth listening experience. Ultimately, the chosen pieces were randomly ordered and spliced to ensure that the influence of any individual composition on the participants was minimized, thereby establishing the definitive final music database (Table 2 ). Table 2 Beat-based Selected Music Library List and Random Splicing Results No. Title Performer Category Tonality BMV Start min Duration (min) Tempo(BPM/Hz) Connected (Major) Connected (Minor) 1 Sonata No.1 in G minor BWV 1001# I- Adagio Itzhak Perlman violin family g minor 1001#1 0:00 1:39 55 4 + 5 + 6 + 8 + 9 1 + 2 + 3 + 7 + 10 2 Partita No.1 in B minor BWV 1002# I- Allemanda Itzhak Perlman violin family b minor 1002#1 2:35 1:31 66 3 Partita No.2 in D minor BWV 1004#III- Sarabanda Itzhak Perlman violin family d minor 1004#3 1:32 1:07 63 4 Partita No.3 in E BWV 1006#II- Loure Itzhak Perlman violin family E major 1006#2 0:00 1:47 78 5 Sonata No.3 in C BWV 1005#II- Fuga Itzhak Perlman violin family C major 1005#2 0:00 1:57 72 6 Sonata No.3 in C BWV 1005#III- Largo Itzhak Perlman violin family C major 1005#3 0:00 1:41 66 7 Unaccompanied Cello Suite No. 2 in D minor, BWV 1008#2 - Prélude MA Youyou violin family d minor 1008#2 0:00 1:53 65 8 Unaccompanied Cello Suite No. 1 in G Major, BWV 1007 - Sarabande MA Youyou violin family G major 1007#1 0:00 1:33 63 9 Unaccompanied Cello Suite No. 6 in D Major, BWV 1012 - Allemande MA Youyou violin family D major 1012#1 0:00 1:44 62 10 Unaccompanied Cello Suite No. 5 in C minor, BWV 1011 MA Youyou violin family c minor 1011#1 0:00 1:28 67 1 Le Clavier bien tempéré - Livre 2: Prélude No.5 en Ré Majeur, BWV 874#5 ZHU Xiaomei piano D major 874#5 0:00 1:27 69 1 + 3 + 5 + 6 + 9 2 + 4 + 7 + 8 + 10 2 Fugue No.8 in E-flat minor, BWV 853 ZHU Xiaomei piano e flat minor 853#8 0:00 1:25 90 3 Goldberg Variations, BWV 988# Aria ZHU Xiaomei piano G major 988 1:03 2:05 57 4 Prélude N° 8 en Mi bémol mineur BWV 853 ZHU Xiaomei piano e flat minor 853#8 0:00 1:31 55 5 Fugue N° 23 en Si majeur BWV 868 ZHU Xiaomei piano B major 868#23 0:00 1:59 73 6 Fugue N° 1 en Do majeur Bwv 846 ZHU Xiaomei piano D major 846#1 0:00 1:50 69 7 Fugue N° 4 en Do dièse mineur Bwv 849 ZHU Xiaomei piano c flat minor 849#4 0:00 1:39 67 8 Fugue N° 6 en Ré mineur Bwv 851 ZHU Xiaomei piano d minor 851#6 0:00 1:39 63 9 Prélude N° 7 en Mi bémol majeur Bwv 852 ZHU Xiaomei piano E flat major 852#7 0:33 1:31 80 10 Johann Sebastian Bach - Prélude N° 8 en Mi bémol mineur Bwv 853 ZHU Xiaomei piano e flat minor 853#8 0:35 1:39 56 2.3 Experimental Design 2.3.1 STAI-S In this study, the State Anxiety Subscale of the State-Trait Anxiety Inventory (STAI-S) was employed to explore the participants' subjective feelings and anxious state. Specifically, the STAI-S was utilized exclusively to assess and compare short-term anxiety states, without addressing the more enduring concept of "trait anxiety." The subscale uses a 4-point Likert scale, where participants are required to rate the intensity of their feelings on a scale ranging from 1 (not at all) to 4 (very much so). The total score on the subscale can range from 20–80, with higher scores indicating a greater level of state anxiety. A score of 40 or above is generally considered indicative of high anxiety levels. 2.3.2 Experimental process During the state anxiety intervention phase, including PA, PI, VA, and VI, participants were instructed to keep their phones on silent mode throughout the experiment to ensure a comfortable and uninterrupted experience. Furthermore, the experiment was conducted in a quiet, well-lit, and temperature-controlled (20–22°C) music therapy room with the use of headphones. The entire experiment comprised five phases: an initial 5-minute resting period with the eyes closed, followed by an 8-minute state anxiety intervention, and a subsequent 5-minute resting period with the eyes closed. The participants completed the State Anxiety Subscale of the State-Trait Anxiety Inventory (STAI-S) before (BF) and after (AF) the experiment, as illustrated in Fig. 1 B. It was mandatory for all participants to complete the STAI-S for both BF and AF to accurately measure and compare their anxiety levels. 2.4 EEG Data Acquisition EEG data were recorded from the subjects during both the resting state and the intervention state, with instructions to remain still throughout the recording process. The experimental data were collected via a 32-channel EEG system, model 8102, manufactured by Delica Medical Equipment Co., Ltd. in Shenzhen, China (Shenzhen Delica). Wet electrodes were utilized for the recording, with FCz serving as the reference electrode for online recording. The electrode cap layout adhered strictly to the international 10–20 system standard, and the sampling rate was set at 1000 Hz. Throughout the experiment, the impedance of all electrodes was maintained below 50 kΩ to ensure the accuracy and reliability of the collected data. 2.5 Data analysis 2.5.1 Experimental Tracks To ascertain the distinctions in spectral distribution between the timbre of a piano and a violin, the experimental audio was analyzed spectrographically via the fast Fourier transform (FFT) method, which specifically targeted the harmonic waveforms: $$\:X\left[k\right]=\sum\:_{n=0}^{N-1}\:x\left[n\right]\cdot\:{e}^{-j\frac{2\pi\:}{N}kn}$$ 2.1 The fundamental frequency was determined by identifying the maximum values in the FFT results, and the magnitudes of the harmonics were subsequently calculated. To enhance clarity and comprehension of the results, the data were averaged for every 10 Hz frequency segment, yielding the average magnitude in dB. Ultimately, the spectral graphs were generated via Matplotlib. The roughness of each experimental audio piece was determined via MATLAB 9.13.0, where the audio signal was segmented into frames. The formula for calculating roughness, which is based on a psychoacoustic model proposed by Daniel and Weber ( 1997 ), is provided below (Eq. 2.2 ). In this formula, R represents the perceived roughness of the entire audio signal, measured with an asper. The term g(z i ​) is a weighted function related to the bark scale (Aures, 1985 ), which is an auditory-based frequency scale that accounts for the human ear's nonlinear perception of different frequencies. $$\:R=\text{cal}\sum\:_{i=1}^{47}\:{\left(g\left({z}_{i}\right)\cdot\:{m}_{i}^{*}\cdot\:{k}_{i-2}\cdot\:{k}_{i}\right)}^{2}\text{[asper]}\text{}$$ 2.2 2.5.2 Preprocessing EEG Data In this study, the EEGLAB toolbox (Delorme & Makeig, 2004 ) implemented in MATLAB 9.13.0 was employed for preprocessing the resting-state EEG data collected during the experiment. A common average reference (CAR) method was applied, and a finite impulse response (FIR) filter was used to perform bandpass filtering between 0.5–45 Hz and notch filtering between 48–52 Hz to increase the signal-to-noise ratio. Additionally, the sampling rate was reduced to 500 Hz to increase computational efficiency and minimize noise impact (Fig. 1 C). To identify and remove ocular and muscular artifacts from the raw EEG signals, the ICLabel tool was utilized (Pion-Tonachini et al., 2019 ). This tool, which is based on an artificial neural network (ANN) framework, automatically labels the source and likelihood of each independent component (IC). A threshold was established to remove ICs with an ocular or muscular artifact probability of 80% or higher. Using ICLabel's automatic function, all artifact components exceeding this threshold were excluded (Fig. 1 C). 2.5.3 EEG Data – Spectral Density Value Analysis The continuous wavelet transform (CWT) was employed to derive the absolute power spectral values across all channels for all the subjects (Fig. 1 D). The wavelet basis function can be mathematically expressed as follows: $$\:{\psi\:}_{a,b}\left(t\right)=\frac{1}{\sqrt{a}}\psi\:\left(\frac{t-b}{a}\right)$$ 2.3 In the formula, a represents the scaling factor, b denotes the translation component, and t is the independent variable. The projection and decomposition of a continuous and finite energy signal x(t) onto wavelet basis functions is defined as the CWT of the signal x(t), expressed as: $$\:W{T}_{x}(a,b)=⟨x\left(t\right),{\psi\:}_{a,b}\left(t\right)⟩=\int\:x\left(t\right){{\psi\:}_{ab}}^{*}\left(t\right)dt=\frac{1}{\sqrt{a}}{\int\:}_{-\infty\:}^{+\infty\:}\:x\left(t\right){\psi\:}^{*}\left(\frac{t-b}{a}\right)dt$$ 2.4 During the S1 (Stable 1), T (Task), and S2 (Stable 2) periods, which were determined across the following frequency bands: beta (14–30 Hz), and gamma (31–44 Hz) (Fig. 1 D). 2.5.4 EEG Data – Brain Source Localization Analysis SLORETA-KEY is frequently used to accurately pinpoint signal origins in low-resolution brain imaging, ensuring that no localization errors occur (Pascual-Marqui et al., 2018 ). In this study, this method was applied to investigate the specific spatial locations where beta and gamma waves undergo the most significant changes in the brain (Fig. 1 D). The current version of the sLoreta software features a spatial resolution of 5x5x5 mm, corresponding to 6,239 voxels. Initially, the beta and gamma waves recorded before and after treatment were truncated to a 1-second segment at 150–151 s. The dataset was subsequently further reduced by selecting 250 to 350 sampling points out of the total 500. The results were analyzed via paired sample t tests and nonparametric permutation tests for the P, V, A, and I groups. 2.6 Statistical analysis Outliers in each dataset were identified and excluded based on Z-scores. To examine within-group differences before and after each intervention, paired t-tests were conducted on both the State-Trait Anxiety Inventory (STAI) scores and power spectral values (Fig. 2 ). To compare changes in parameters across groups, a one-way analysis of variance (ANOVA) was performed on the percentage decreases in the P, V, A, and I components (Fig. 3 ), in order to determine whether significant differences existed among groups. When significant effects were observed, pairwise comparisons between groups were conducted using unpaired t-tests. Prior to conducting parametric analyses, the assumption of homogeneity of variances was assessed using Levene’s test. The normality of data distribution was evaluated using the Shapiro-Wilk test, which is particularly suitable for small sample sizes. For datasets that violated the assumption of normality, between-group comparisons were performed using the Kruskal-Wallis test. To control the family-wise error rate, the Bonferroni correction was applied to post hoc comparisons following parametric tests. For both parametric and non-parametric cases, Tukey’s HSD or Dunn’s test, respectively, was used for post hoc analysis. All statistical analyses were performed using GraphPad Prism (version 10.1.2), with statistical significance set at p < 0.05. 3. Results 3.1 Experimental Music Significantly Reduces Anxiety To preliminarily investigate whether selected music can alleviate anxiety, one-way ANOVA was conducted on the pre- and postexperimental STAI-S scores of both the anxiety and nonanxiety groups (Fig. 2 A and B). As anticipated, anxiety scores demonstrated a decreasing trend across all groups, with a significant main effect observed for the music condition in both the anxiety group (F (7,68) = 3.175, P = 0.0058) and the control group (F (7,52) = 5.464, P < 0.0001). These results suggest that classical music, selected within a specific range of BPMs, has anxiety-reducing capabilities. To minimize the influence of human factors, we analyzed the spectral density values of beta and gamma brain waves via one-way ANOVA (Fig. 2 C − 2F). For both brain waves, only in the control group did the music condition have a significant main effect on the target variable (Fig. 2 D: F (7, 192) = 2.834, P = 0.0078; Fig. 2 F: F (7, 257) = 2.209, P = 0.034). In the anxiety group, beta wave power significantly decreased in the PA, VA, and VI groups, whereas gamma wave power significantly decreased in the VI group, indicating reduced anxiety levels. Notably, the VA group presented the most substantial decrease ( P < 0.0001), and the VI group presented significant reductions in both the beta and gamma waves (Fig. 2 C − 2F). 3.2 Major Violin Music Significantly Alleviates Anxiety. To delve deeper into whether a single factor was responsible for the significant changes observed in the two-factor (timbre and tonality) groups, we isolated the four elements P, V, A, and I (where P = PA + PI, V = VA + VI, A = PA + VA, and I = PI + VI) and conducted a statistical analysis (Fig. 3 A and B). Notably, in the Anxiety group, there was a significant preference for major music ( P = 0.03) and violin music ( P = 0.0261). Conversely, in the control group, minor music (A: P = 0.0055; B: P = 0.0041) and piano music (P = 0.0306) led to a significant reduction in gamma waves (Table 3 & Fig. 3 ). These findings suggest that major key violin music significantly reduces anxiety in individuals with high anxiety levels. Table 3 Changes in Power Spectral Density of Separated Factors P, V, A, and I before and after music interventions in Beta Brain Waves and Gamma Brain Wave. Group Sample Size (N) Effect Size (η²) Test Power Mean difference Standard Deviation (SD) t Value Degrees of Freedom (df) p Value Beta Wave ANXIETY-P 76 0.21 0.81 2.669 4.701 0.6628 149 0.5085 ANXIETY-V 75 ANXIETY-A 79 -8.278 3.777 2.191 147 0.03* ANXIETY-I 70 CONTROL-P 70 0.4 0.99 4.868 5.068 0.9605 130 0.3386 CONTROL-V 62 CONTROL-A 75 14.38 4.918 2.923 170 0.0041* CONTROL-I 67 Gamma Wave ANXIETY-P 72 0.44 0.99 10.53 4.684 2.248 141 0.0261* ANXIETY-V 71 ANXIETY-A 73 0.0246 6.334 0.03887 148 0.969 ANXIETY-I 77 CONTROL-P 69 0.23 0.85 -12.75 5.833 2.186 130 0.0306* CONTROL-V 63 CONTROL-A 67 19 5.515 3.445 129 0.0008* CONTROL-I 64 3.3 Violin Music Exhibits greater frequency density and roughness than does piano Music. To gain a deeper understanding of how different musical elements influence anxiety levels, we employed the fast Fourier transform (FFT) to analyze the harmonic spectrum of each experimental track. Distinct waveform differences between the piano group (Fig. 4 A and B) and the violin group (Fig. 4 C and D) were evident in the highlighted squares. To emphasize the significance of these differences, we conducted a statistical analysis of the roughness and power density of frequency between the two groups. Notably, the violin group presented significantly greater roughness and greater frequency power density than did the piano group ( P < 0.0001), suggesting that the acoustic characteristics of these two instruments in the high-frequency range are markedly distinct (Fig. 4 E and F). 3.4 Major Violin Music Activates a Broad Range of Cortical Regions in Emotion Regulation Given the profound anxiety-reducing effects of major violin music, we conducted a source localization analysis to explore the spatial patterns underlying these changes. The sLORETA analysis revealed that, in the anxiety group, the violin elements primarily activated cortices associated with cognitive and emotional processing. In contrast, in the control group, the activated brain areas involved mainly the somatosensory function-related inferior parietal lobule and primary somatosensory cortex. Notably, both the anxiety group and the control group presented significant prefrontal cortex activity in response to piano music. Furthermore, in the anxiety group, minor elements distinctly activated the emotional cognitive cortex, whereas in the control group, major elements clearly activated the same region. Changes in the other groups were primarily associated with the somatosensory cortex. Similar patterns were observed in gamma wave activity, with the notable difference that major elements specifically activated the emotion-regulating cortex. Additionally, the gamma wave engaged a broader range of cortices than did the beta wave (Table 4 ). Table 4 The Activated Brain Regions in Beta Wave and Gamma Wave Intervened by Different Musical Elements. Group Broadman Area number Specific Location Beta Wave Violin Anxiety BA 9, 32 Dorsolateral Prefrontal Cortex, Dorsal Anterior Cingulate Cortex Control BA 40, 2 Supramarginal Gyrus, Primary Somatosensory Cortex Piano Anxiety BA 9, 10 Dorsolateral Prefrontal Cortex, Anterior Prefrontal Cortex Control BA 9, 10 Dorsolateral Prefrontal Cortex, Anterior Prefrontal Cortex Major Anxiety BA 7, 40 Superior Parietal Lobule, Supramarginal Gyrus Control BA 7, 31 Superior Parietal Lobule, Dorsal Posterior Cingulate Cortex Minor Anxiety BA 47#, 11# Inferior Frontal Gyrus, Orbitofrontal Cortex Control BA 40, 2 Supramarginal Gyrus, Primary Somatosensory Cortex Gamma Wave Violin Anxiety BA 6#, 9#, 32# Premotor Cortex, Dorsolateral Prefrontal Cortex, Dorsal Anterior Cingulate Cortex Control BA 23, 31, 10 Ventral Posterior Cingulate Cortex, Dorsal Posterior Cingulate Cortex, Anterior Prefrontal Cortex Piano Anxiety BA 13#, 47#, 38# Insular Cortex, Inferior Frontal Gyrus, Temporal Pole Control BA 32#, 24 Dorsal Anterior Cingulate Cortex, Ventral Anterior Cingulate Cortex Major Anxiety BA 32#, 24 Dorsal Anterior Cingulate Cortex, Ventral Anterior Cingulate Cortex Control BA 5, 7, 13# Somatosensory Association Cortex, Superior Parietal Lobule, Insular Cortex Minor Anxiety BA 40, 2 Supramarginal Gyrus Control BA 19#, 30*# Visual Association Cortex, Retrosplenial Cortex # indicates the right brain area. P = PA + PI; V = VA + VI; A = PA + VA; I = PI + VI. Abbreviations: BA, The Broadmann Area. 3.5 Major Violin Music Alleviates Anxiety by Activating the dACC and DLPFC. To delve deeper into the neural mechanisms through which violin-based anxiety (VA) music alleviates anxiety, we utilized sLORETA analysis to examine the differences in current density following the VA music intervention. The gamma wave results demonstrated significant activation in the dorsal anterior cingulate cortex (dACC) and dorsolateral prefrontal cortex (DLPFC) (t = 3.674, P = 0.03540; Fig. 5 A). In contrast, when piano-based intervention (PI) music was examined, no significant brain region activation was observed postintervention, with the most responsive areas centered around the visuomotor coordination region and the supramarginal gyrus implicated in phonology (Fig. 5 B). These findings further imply that VA music may effectively mitigate anxiety by activating the dACC and DLPFC regions. 4. Discussion Music therapy holds immense potential for alleviating anxiety, yet the physical attributes of music and the underlying neural circuits involved remain largely unknown. This study aimed to investigate the effects and mechanisms of music with varying emotions (major and minor) and instruments (piano and violin) on anxiety relief. A key innovative finding is that violin music, which is characterized by greater roughness and energy, is particularly effective in reducing anxiety. One major challenge in music therapy lies in the intrinsic complexity of music itself, which comprises multiple elements whose variability is difficult to control. We selected music by Bach, as Baroque compositions are known for their relatively uniform style and are less subject to the expressive fluctuations seen in other musical periods (Sharda et al., 2019 ). Additionally, we constructed the experimental track by splicing together several ordinary musical phrases rather than using complete pieces, which was done to avoid the emotional progression, climax, and structural development that a full composition typically contains (Fedorenko et al., 2012 ). Finally, the tempos to a specific BPM were constrained within the human resonance frequency range to minimize the physiological effects of rhythm on participants (MacDougall & Moore, 2005 ). Music therapy has shown promise across various clinical settings (Tang et al., 2021 ). To validate its anxiety-reducing effects, this study recorded participants' scale data and electroencephalogram (EEG) data before and after music interventions. Notably, all participants experienced significant anxiety relief following the interventions (Fig. 2 ), indicating that the selected music database generally possesses anxiety-reducing properties, which aligns with prior research (Barlas et al., 2023 ; Weineck et al., 2022 ). Tonality, a fundamental aspect of music, plays a pivotal role in emotional communication (Balkwill & Thompson, 1999 ), with major and minor keys often associated with happiness and sadness, respectively. To assess the impact of musical emotions on anxiety, we conducted interventions using music with different tonalities on anxious individuals and analyzed the data via scale scores and EEG power spectral density. Our analysis revealed that individuals in control group preferred minor key music over major key music. Conversely, anxious individuals responded more favorably to major key music, experiencing a more pronounced anxiety-relieving effect (Fig. 3 A). Anxiety is a high-energy emotion; similarly, major key music is considered high-energy because of its capacity to evoke heightened arousal (Juslin & Laukka, 2004 ; Punkanen et al., 2011 ; Ramirez & Vamvakousis, 2012 ). Previous research by Yoon et al. ( 2020 ) revealed that low-energy emotions tend to favor low-energy music, and vice versa. Furthermore, high-energy, danceable music rich in lyrics has been shown to alleviate anxiety induced by pain (Howlin & Rooney, 2021 ; Hsieh et al., 2014 ), supporting our findings. We hypothesize that the control group may prefer minor key music because its low-energy characteristics induce calmness. In contrast, anxious individuals, driven by their high-energy emotional inertia, are more susceptible to distraction by high-energy music, thereby reducing anxiety. Timbre stands as a pivotal characteristic in the realm of music. To delve deeper into how various timbres influence emotions, this study explored the impact of piano and violin music on anxiety relief. Intriguingly, the results revealed a discernible preference for musical instruments among the anxious cohort versus the control group. Specifically, violin music had a more potent anxiety-alleviating effect on the anxious group, whereas piano music was more efficacious for the control group, as illustrated in Fig. 3 B. The soothing effect of piano music on emotions and its stress-relieving capabilities have garnered widespread acknowledgment (Musa et al., 2022 ; Toyoshima et al., 2011 ). Conversely, the preference for violin music among anxious individuals presents an unexpected twist. To gain a clearer understanding of the factors driving these divergent outcomes, a spectral analysis of the experimental tracks was conducted. The findings revealed that violin music boasts a higher frequency density and greater roughness, as depicted in Fig. 4 . Given that roughness contributes to musical disharmony, our findings contradict traditional wisdom that harmonious music is superior in alleviating emotional stress (Bodner et al., 2007 ; Štillová et al., 2021 ). Notably, the music employed in this study was not "dissonant" but rather featured variations in timbre. We theorize that the less harmonious sound prompts cognitive interference, thereby shifting attention and enabling individuals to discharge internal tension. Mogg and Bradley ( 1998 ) established that less harmonious music disrupts cognitive performance more than harmonious music does. In light of anxious individuals' tendency to exhibit heightened focus on specific stimuli, rough music may clash with their sensory information (McDermott et al., 2010 ; Tenney, 1988 ), compelling them to redirect their attention to the music, thereby alleviating anxiety through distraction. Given the combined influence of beta and gamma wave alterations on the results, a deeper dive into the distinct effects of these wave types was undertaken. The sLORETA outcomes highlighted that, in the violin group, regions BA 9 and 32 exhibited increased activity among anxious participants (Table 4 ), suggesting that violin music significantly impacts emotional and cognitive states, potentially mitigating anxiety symptoms. Conversely, the impact of violin music on the cognition of the population in control group was minimal, with notable effects observed in the somatosensory cortex. These findings reinforce the hypothesis that violin music serves as a more potent emotional intervention for anxious individuals. In the piano group, both anxious and normal participants demonstrated beta-wave activity within the dorsomedial prefrontal cortex (dmPFC) (Table 4 ). Considering dmPFC dysregulation plays a pivotal role in anxiety pathogenesis (Rosenkranz et al., 2003 ), piano music may modulate anxiety across various emotional states through the dmPFC. An analysis of major and minor key elements revealed that anxious individuals exhibited notable fluctuations in brain regions associated with emotional regulation after exposure to minor key music (Table 4 ). To ensure that beta wave behavior is not influenced by gamma waves, this observation must be integrated with a gamma wave analysis. When analyzed in conjunction with gamma wave data, major key music had a more pronounced impact on emotional regulation than minor key music among individuals experiencing anxiety. The divergence in the results between gamma and beta waves could stem from the inhibitory influence of gamma waves on beta waves. Lundqvist et al. ( 2016 ) suggested that heightened gamma wave activity is commonly linked to the encoding or decoding of information, accompanied by a suppression of beta wave activity. Given the extensive reach of gamma waves across multiple Brodmann areas and their strong connections to advanced emotional processing and cognitive control regions, they may comprehensively mirror alterations in brain function during anxiety. To delve deeper into the neural mechanisms underlying the anxiety-alleviating effects of VA music, we conducted a brain source localization analysis postintervention with VA and PI music, with a focus on gamma brain waves. Notably, significant activation was observed in the dorsal anterior cingulate cortex (dACC) and dorsolateral prefrontal cortex (DLPFC) following the VA music intervention (Fig. 5 ). The dACC plays a pivotal role in the genesis of anxiety and fear, with its activation linked to overgeneralization in loss scenarios (Etkin et al., 2011 ). We propose that the heightened dACC activation during anxiety alleviation may represent an adaptive regulatory mechanism, potentially fostering adaptive responses. Moreover, the DLPFC is often hypoactive in depression, and its activation is intricately associated with antidepressant effects (Fox et al., 2012 ). Given the neural overlap between anxiety and depression, the enhanced DLPFC activation following anxiety relief may underscore its vital role in emotional regulation. Furthermore, this augmented activation may inhibit the subgenual cingulate cortex, thereby mitigating anxiety (Fox et al., 2012 ). In essence, VA music alleviates anxiety through the engagement of both the dACC and the DLPFC. 5. Limitations Several limitations of the present study should be acknowledged. First, the absence of a true control group, which is no-treatment condition, restricts the interpretability of the results. While the study compared intervention groups with different levels of state anxiety, the lack of a non-intervention baseline makes it difficult to determine whether the observed effects were solely due to the interventions. As the state emotion changes with time, it would not be comparable to expose the same participant to both a music intervention and a non-intervention condition at different times, which highlights a broader methodological limitation in human experiments involving affective states. Second, while efforts were made to control various musical variables, such as BPM and the consistent compositional style of Bach, other expressive and acoustic features of the music remained difficult to standardize. Musical expressivity is complex and multi-dimensional by nature, and its impact on listeners can vary widely (Epp, 2007 ; Wheeler, 2016 ). This challenge is not unique to our study but reflects a broader limitation in music therapy research, where complete control over all auditory and emotional variables is inherently constrained. Finally, for the participants recruitment, the sample size of each group was relatively small, which may have limited the statistical power of the analyses and the generalizability of the findings. Also, there was a gender imbalance in the participant pool, with a higher number of female participants. Given that men and women may respond differently to emotional and musical stimuli (Nater et al., 2006 ), this imbalance could have influenced the results. Future studies should aim for a more balanced gender distribution to enhance the representativeness and reliability of findings. 6. Conclusion This study offers novel insights into the role of timbre and tonality in anxiety relief. By comparing power spectral density values with various musical features, we found that violin music in a major key elicited the strongest responses. The neural mechanism underlying this effect likely involves the activation of the dorsal anterior cingulate cortex (dACC) and dorsolateral prefrontal cortex (DLPFC). Additionally, our findings suggest that gamma waves may be more appropriate than beta waves for studying high anxiety states. Our research sheds light on the relationship between musical elements and anxiety in individuals, providing a fresh perspective on anxiety alleviation techniques and deepening our understanding of how timbre and pitch can be harnessed to manage anxiety effectively. Declarations Ethics approval statement This study received approval from the Ethics Committee of Biology and Medicine at Northwestern Polytechnical University. (Approval No: 202302054) Patient consent statement All participants provided written informed consent and were given detailed information regarding the study. Appropriate compensation was provided, and the confidentiality of personal data was strictly maintained. Data availability statement The datasets utilized and examined in this study can be obtained from the corresponding author upon reasonable request. Conflict of interest disclosure The authors declare that there are no conflicts of interest regarding the publication of this paper. Funding Statement This publication was supported by Hong Kong Polytechnic University through the Undergraduate Research and Innovation Scheme (Project ID: P0047931) and the Fundamental Research Funds for the Central Universities (Grant No. D5000230188). Authors’ Contributions Q. LUO was involved in conceptualization, methodology, software, formal analysis, investigation, writing, writing-review & editing, visualization, and funding acquisition. K. YANG contributed to methodology and software as well as formal analysis. Y. WANG participated in methodology, software, investigation, and writing. W. ZHANG, M. XI, A. QIAN and P. NIE provided resources. A. QIAN also took part in writing-review & editing and funding acquisition. G. CHEN was responsible for supervision. All authors read and approved the final manuscript. References Aures, W. (1985). Berechnungsverfahren für den sensorischen Wohlklang beliebiger Schallsignale. Acta Acustica united with Acustica , 59 (2), 130-141. Balkwill, L.-L., & Thompson, W. F. (1999). A cross-cultural investigation of the perception of emotion in music: Psychophysical and cultural cues. Music perception , 17 (1), 43-64. Barlas, T., Sodan, H. N., Avci, S., Cerit, E. T., & Yalcin, M. M. (2023). The impact of classical music on anxiety and pain perception during a thyroid fine needle aspiration biopsy. Hormones (Athens) , 22 (4), 581-585. https://doi.org/10.1007/s42000-023-00473-3 Bodner, E., Gilboa, A., & Amir, D. (2007). The unexpected side-effects of dissonance. Psychology of Music , 35 (2), 286-305. Chen, H., Yang, J., Zhang, B., Zhang, L., Wang, J., Zhang, H., Zhang, H., Yu, C., Zhang, J., & Xu, Z. (2025). Quantitative analysis of the resting-state EEG power spectrum in patients with epilepsy comorbid with anxiety and depression. Acta Epileptol , 7 (1), 22. https://doi.org/10.1186/s42494-025-00206-6 Daniel, P., & Weber, R. (1997). Psychoacoustical roughness: Implementation of an optimized model. Acta Acustica united with Acustica , 83 (1), 113-123. Davidson, R. J. (2000). Affective style, psychopathology, and resilience: brain mechanisms and plasticity. American psychologist , 55 (11), 1196. Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods , 134 (1), 9-21. https://doi.org/10.1016/j.jneumeth.2003.10.009 Endler, N. S., & Kocovski, N. L. (2001). State and trait anxiety revisited. Journal of anxiety disorders , 15 (3), 231-245. Epp, E. (2007). Locating the autonomous voice: Self-expression in music-centered music therapy. Voices: A world forum for music therapy, Etkin, A., Egner, T., & Kalisch, R. (2011). Emotional processing in anterior cingulate and medial prefrontal cortex. Trends Cogn Sci , 15 (2), 85-93. https://doi.org/10.1016/j.tics.2010.11.004 Fedorenko, E., McDermott, J. H., Norman-Haignere, S., & Kanwisher, N. (2012). Sensitivity to musical structure in the human brain. Journal of neurophysiology , 108 (12), 3289-3300. Fox, M. D., Buckner, R. L., White, M. P., Greicius, M. D., & Pascual-Leone, A. (2012). Efficacy of transcranial magnetic stimulation targets for depression is related to intrinsic functional connectivity with the subgenual cingulate. Biol Psychiatry , 72 (7), 595-603. https://doi.org/10.1016/j.biopsych.2012.04.028 Ginsburg, G. S., Becker, E. M., Keeton, C. P., Sakolsky, D., Piacentini, J., Albano, A. M., Compton, S. N., Iyengar, S., Sullivan, K., & Caporino, N. (2014). Naturalistic follow-up of youths treated for pediatric anxiety disorders. JAMA psychiatry , 71 (3), 310-318. Harder, H. G., Wagner, S., & Rash, J. (2016). Mental illness in the workplace: Psychological disability management . Routledge. Howlin, C., & Rooney, B. (2021). Patients choose music with high energy, danceability, and lyrics in analgesic music listening interventions. Psychology of Music , 49 (4), 931-944. Hsieh, C., Kong, J., Kirsch, I., Edwards, R. R., Jensen, K. B., Kaptchuk, T. J., & Gollub, R. L. (2014). Well-loved music robustly relieves pain: a randomized, controlled trial. PLoS One , 9 (9), e107390. https://doi.org/10.1371/journal.pone.0107390 Juslin, P. N., & Laukka, P. (2004). Expression, perception, and induction of musical emotions: A review and a questionnaire study of everyday listening. Journal of new music research , 33 (3), 217-238. Keil, A., Müller, M. M., Gruber, T., Wienbruch, C., Stolarova, M., & Elbert, T. (2001). Effects of emotional arousal in the cerebral hemispheres: a study of oscillatory brain activity and event-related potentials. Clin Neurophysiol , 112 (11), 2057-2068. https://doi.org/10.1016/s1388-2457(01)00654-x Kocsis, R. N. (2013). Book Review: Diagnostic and Statistical Manual of Mental Disorders: Fifth Edition (DSM-5). International Journal of Offender Therapy and Comparative Criminology , 57 (12), 1546-1548. https://doi.org/10.1177/0306624x13511040 Lu, G., Jia, R., Liang, D., Yu, J., Wu, Z., & Chen, C. (2021). Effects of music therapy on anxiety: A meta-analysis of randomized controlled trials. Psychiatry research , 304 , 114137. Lundqvist, M., Rose, J., Herman, P., Brincat, S. L., Buschman, T. J., & Miller, E. K. (2016). Gamma and Beta Bursts Underlie Working Memory. Neuron , 90 (1), 152-164. https://doi.org/10.1016/j.neuron.2016.02.028 MacDougall, H. G., & Moore, S. T. (2005). Marching to the beat of the same drummer: the spontaneous tempo of human locomotion. Journal of applied physiology , 99 (3), 1164-1173. McDermott, J. H., Lehr, A. J., & Oxenham, A. J. (2010). Individual differences reveal the basis of consonance. Curr Biol , 20 (11), 1035-1041. https://doi.org/10.1016/j.cub.2010.04.019 Mogg, K., & Bradley, B. P. (1998). A cognitive-motivational analysis of anxiety. Behaviour research and therapy , 36 (9), 809-848. Müller, M. M., Keil, A., Gruber, T., & Elbert, T. (1999). Processing of affective pictures modulates right-hemispheric gamma band EEG activity. Clin Neurophysiol , 110 (11), 1913-1920. https://doi.org/10.1016/s1388-2457(99)00151-0 Musa, A., Ng, Q. X., Wai, Y. Z., & Iqbal, T. (2022). Effect of slow tempo music on markers of anxiety during cataract surgery: Randomized control trial. Taiwan J Ophthalmol , 12 (1), 74-81. https://doi.org/10.4103/tjo.tjo_10_21 Nater, U. M., Abbruzzese, E., Krebs, M., & Ehlert, U. (2006). Sex differences in emotional and psychophysiological responses to musical stimuli. Int J Psychophysiol , 62 (2), 300-308. https://doi.org/10.1016/j.ijpsycho.2006.05.011 Nieminen, S., Istók, E., Brattico, E., & Tervaniemi, M. (2012). The development of the aesthetic experience of music: Preference, emotions, and beauty. Musicae Scientiae , 16 (3), 372-391. Nilsson, U. (2008). The anxiety-and pain-reducing effects of music interventions: a systematic review. AORN journal , 87 (4), 780-807. Oya, H., Kawasaki, H., Howard, M. A., 3rd, & Adolphs, R. (2002). Electrophysiological responses in the human amygdala discriminate emotion categories of complex visual stimuli. J Neurosci , 22 (21), 9502-9512. https://doi.org/10.1523/jneurosci.22-21-09502.2002 Pascual-Marqui, R. D., Faber, P., Kinoshita, T., Kochi, K., Milz, P., Keiichiro, N., & Yoshimura, M. (2018). A comparison of bivariate frequency domain measures of electrophysiological connectivity. bioRxiv , 459503. Pion-Tonachini, L., Kreutz-Delgado, K., & Makeig, S. (2019). ICLabel: An automated electroencephalographic independent component classifier, dataset, and website. Neuroimage , 198 , 181-197. https://doi.org/10.1016/j.neuroimage.2019.05.026 Punkanen, M., Eerola, T., & Erkkilä, J. (2011). Biased emotional preferences in depression: Decreased liking of angry and energetic music by depressed patients. Music and Medicine . Ramirez, R., & Vamvakousis, Z. (2012). Detecting emotion from EEG signals using the emotive epoc device. International Conference on Brain Informatics, Rosenkranz, J. A., Moore, H., & Grace, A. A. (2003). The prefrontal cortex regulates lateral amygdala neuronal plasticity and responses to previously conditioned stimuli. J Neurosci , 23 (35), 11054-11064. https://doi.org/10.1523/jneurosci.23-35-11054.2003 Sethares, W. A. (2005). Tuning, timbre, spectrum, scale . Springer Science & Business Media. Sharda, N., Mattoon, E., Matters, L., Prewitt, J., McDonald, S., Sloane, R., Cassas, C., & White, H. (2019). Bach to the Basics: Implementation and Impact of a Postoperative, Inpatient Personalized Music Program for Older Adults. J Perianesth Nurs , 34 (2), 347-353. https://doi.org/10.1016/j.jopan.2018.05.006 Štillová, K., Kiska, T., Koriťáková, E., Strýček, O., Mekyska, J., Chrastina, J., & Rektor, I. (2021). Mozart effect in epilepsy: Why is Mozart better than Haydn? Acoustic qualities-based analysis of stereoelectroencephalography. Eur J Neurol , 28 (5), 1463-1469. https://doi.org/10.1111/ene.14758 Swain, J., Hancock, K., Hainsworth, C., & Bowman, J. (2013). Acceptance and commitment therapy in the treatment of anxiety: a systematic review. Clinical psychology review , 33 (8), 965-978. Szuhany, K. L., & Simon, N. M. (2022). Anxiety Disorders: A Review. JAMA , 328 (24), 2431-2445. https://doi.org/10.1001/jama.2022.22744 Tang, H., Chen, L., Wang, Y., Zhang, Y., Yang, N., & Yang, N. (2021). The efficacy of music therapy to relieve pain, anxiety, and promote sleep quality, in patients with small cell lung cancer receiving platinum-based chemotherapy. Supportive care in cancer , 29 (12), 7299-7306. Tenney, J. (1988). A history of consonance and dissonance. Toyoshima, K., Fukui, H., & Kuda, K. (2011). Piano playing reduces stress more than other creative art activities. International Journal of Music Education , 29 (3), 257-263. Vanhollebeke, G., De Smet, S., De Raedt, R., Baeken, C., van Mierlo, P., & Vanderhasselt, M. A. (2022). The neural correlates of psychosocial stress: A systematic review and meta-analysis of spectral analysis EEG studies. Neurobiol Stress , 18 , 100452. https://doi.org/10.1016/j.ynstr.2022.100452 Weich, S., Pearce, H. L., Croft, P., Singh, S., Crome, I., Bashford, J., & Frisher, M. (2014). Effect of anxiolytic and hypnotic drug prescriptions on mortality hazards: retrospective cohort study. Bmj , 348 . Weineck, K., Wen, O. X., & Henry, M. J. (2022). Neural synchronization is strongest to the spectral flux of slow music and depends on familiarity and beat salience. Elife , 11 . https://doi.org/10.7554/eLife.75515 Wheeler, B. L. (2016). 720Music Therapy Research: An Overview. In J. Edwards (Ed.), The Oxford Handbook of Music Therapy (pp. 0). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199639755.013.11 Yang, Y., Zhang, X., Zhu, Y., Dai, Y., Liu, T., & Wang, Y. (2015). Cognitive impairment in generalized anxiety disorder revealed by event-related potential N270. Neuropsychiatr Dis Treat , 11 , 1405-1411. https://doi.org/10.2147/ndt.S84666 Yoon, S., Verona, E., Schlauch, R., Schneider, S., & Rottenberg, J. (2020). Why do depressed people prefer sad music? Emotion , 20 (4), 613-624. https://doi.org/10.1037/emo0000573 Zhu, X., Lazarov, A., Dolan, S., Bar-Haim, Y., Dillon, D. G., Pizzagalli, D. A., & Schneier, F. (2023). Resting state connectivity predictors of symptom change during gaze-contingent music reward therapy of social anxiety disorder. Psychological medicine , 53 (7), 3115-3123. Zung, W. W. (1971). A rating instrument for anxiety disorders. Psychosomatics: Journal of Consultation and Liaison Psychiatry . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 06 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 24 Aug, 2025 Reviews received at journal 21 Aug, 2025 Reviews received at journal 19 Aug, 2025 Reviewers agreed at journal 17 Aug, 2025 Reviews received at journal 11 Aug, 2025 Reviewers agreed at journal 06 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers agreed at journal 02 Aug, 2025 Reviewers agreed at journal 01 Aug, 2025 Reviewers invited by journal 01 Aug, 2025 Editor invited by journal 07 Jul, 2025 Editor assigned by journal 30 Jun, 2025 Submission checks completed at journal 27 Jun, 2025 First submitted to journal 12 Jun, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6879305","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":498883159,"identity":"fc3aaf7a-3575-4116-b799-3fb1c3135422","order_by":0,"name":"Qianwen LUO","email":"","orcid":"","institution":"The Hong Kong Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Qianwen","middleName":"","lastName":"LUO","suffix":""},{"id":498883160,"identity":"c966b86f-5963-4bb5-a0f7-993748ab9771","order_by":1,"name":"Yifan WANG","email":"","orcid":"","institution":"The Hong Kong Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Yifan","middleName":"","lastName":"WANG","suffix":""},{"id":498883162,"identity":"436ebf40-ff2f-4ec0-98c7-f1ff39612a77","order_by":2,"name":"Kairui YANG","email":"","orcid":"","institution":"The Hong Kong Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Kairui","middleName":"","lastName":"YANG","suffix":""},{"id":498883163,"identity":"ed61a6d6-dfa2-47f3-b772-aa628ea4182f","order_by":3,"name":"Airong QIAN","email":"","orcid":"","institution":"Northwestern Polytechnical University","correspondingAuthor":false,"prefix":"","firstName":"Airong","middleName":"","lastName":"QIAN","suffix":""},{"id":498883165,"identity":"47302eb6-a068-445b-a8c7-03da0f211bf5","order_by":4,"name":"Pei NIE","email":"","orcid":"","institution":"Hospital of Northwestern Polytechnical University","correspondingAuthor":false,"prefix":"","firstName":"Pei","middleName":"","lastName":"NIE","suffix":""},{"id":498883166,"identity":"fed3c168-b1aa-444a-bf82-3ace46ace860","order_by":5,"name":"Min XI","email":"","orcid":"","institution":"Hospital of Northwestern Polytechnical University","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"XI","suffix":""},{"id":498883169,"identity":"273e5a4f-b5af-4c0d-91b7-0e76b99f08ff","order_by":6,"name":"Wenjuan ZHANG","email":"data:image/png;base64,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","orcid":"","institution":"Northwestern Polytechnical University","correspondingAuthor":true,"prefix":"","firstName":"Wenjuan","middleName":"","lastName":"ZHANG","suffix":""},{"id":498883170,"identity":"13f98d7b-03d6-4ed1-95bb-dc7d8be56e85","order_by":7,"name":"Gong CHEN","email":"","orcid":"","institution":"The Hong Kong Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Gong","middleName":"","lastName":"CHEN","suffix":""}],"badges":[],"createdAt":"2025-06-12 10:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6879305/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6879305/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-31161-4","type":"published","date":"2025-12-06T15:57:36+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88896838,"identity":"fad9022e-217c-4859-baa1-435325cba4ce","added_by":"auto","created_at":"2025-08-12 13:09:56","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":703720,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental Design and Procedure for the EEG Experiment. A. Steps for processing music materials; B. Overall experiment flowchart; C. EEG data preprocessing process. D. EEG data analysis process. Abbreviations: EEG, electroencephalogram. BWV, Bach Werke Verzeichnis\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6879305/v1/5ee7b0e74c3f81c6758fc13e.jpg"},{"id":88899182,"identity":"ea173214-e5ae-4d57-8a14-3835c43683cd","added_by":"auto","created_at":"2025-08-12 13:25:56","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":361850,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of Music Interventions on STAI-S Scores and Brain Wave Power Density in Anxiety and Control Groups. (A, B) Changes in the STAI-S scores before and after the intervention in the anxiety group (A) and control group (B). (C, D) Power density of gamma waves in the anxiety group (C) and control group (D). (E, F) Power density of beta waves in the anxiety group (E) and control group (F). * P \u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01, *** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, **** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001. Abbreviations: PA, piano major; PI, piano minor; VA, violin major; VI, violin minor; P, piano; V, violin; A, major; I, minor; BF, before; AF, after. (n = 8-11)\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6879305/v1/9b3daec484fcaec61c4c3b58.jpg"},{"id":88894189,"identity":"aa884dfe-af9b-4d2d-b7e7-e5f24a6b4470","added_by":"auto","created_at":"2025-08-12 13:01:56","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":261455,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the spectral density of separated factors P, V, A, and I before and after music intervention in beta-brain waves (A) and gamma-brain waves (B). *\u003cem\u003e P\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003e P\u003c/em\u003e\u0026lt; 0.0001. P = PA + PI; V = VA + VI; A = PA + VA; I = PI + VI. Abbreviations: PA, piano major; PI, piano minor; VA, violin major; VI, violin minor; P, piano; V, violin; A, major; I, minor.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6879305/v1/75490faf9b25510f9ef13dcb.jpg"},{"id":88894190,"identity":"89318d4f-fb0d-4035-8f04-b18620a7e07a","added_by":"auto","created_at":"2025-08-12 13:01:56","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":636209,"visible":true,"origin":"","legend":"\u003cp\u003eSpectral analysis of the harmonics for experimental tracks and statistical outcomes. (A-D) Spectrograms of the PA (A), PI (B), VA (C), and VI (D) experimental tracks averaged every 10 Hz. (E-F) Roughness (E) and power density of frequency (F) between piano pieces and violin pieces in the 4000 Hz--5000 Hz range. * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003e P\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003e P\u003c/em\u003e \u0026lt; 0.0001. Abbreviations: PA, piano major; PI, piano minor; VA, violin major; VI, violin minor; FFT: fast Fourier transform\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6879305/v1/31af51abceaaa29ec24d8407.jpg"},{"id":88894199,"identity":"960083c1-3449-4715-80b0-260e2b2b97e3","added_by":"auto","created_at":"2025-08-12 13:01:57","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":898575,"visible":true,"origin":"","legend":"\u003cp\u003eXYZ LORETA slices of the average change ingamma waves in the anxiety group followingintervention with VA music (A) and PI music (B); redvoxels indicate areas where PPF activation \u0026gt; PPI activation. The coordinates in the image represent the most prominently activated locations. Abbreviations: PI, piano minor; VA, violin major.\u003c/p\u003e","description":"","filename":"Figure5clear.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6879305/v1/60c7111d0e78fbf4e9b59a36.jpg"},{"id":97724479,"identity":"a934d849-08d0-4bf8-b069-8702e34882d7","added_by":"auto","created_at":"2025-12-08 16:12:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4139871,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6879305/v1/8814193f-0046-43e9-90a5-b4d59cb9de50.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Violin Major Music Alleviates University Students’ Anxiety Maybe through the dACC and DLPFC Circuits","fulltext":[{"header":"1. Background","content":"\u003cp\u003eState anxiety is a prevalent emotion that, if left unmanaged, can escalate into anxiety disorders, potentially leading to severe health issues such as migraines, cardiovascular diseases, and even cancer (Szuhany \u0026amp; Simon, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The economic impact of anxiety is substantial, with annual healthcare costs and productivity losses estimated to surpass \u003cspan\u003e$\u003c/span\u003e4\u0026nbsp;billion (Harder et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, early intervention for individuals experiencing anxiety is imperative to prevent its progression into a disorder. University students, with their heightened awareness of personal, interpersonal, and sociocultural differences, coupled with their openness to change, are particularly susceptible to psychological issues that can significantly impair cognitive functions such as thinking, perception, and learning (Endler \u0026amp; Kocovski, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Kocsis, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Addressing anxiety among university students is thus crucial for their academic success and overall well-being.\u003c/p\u003e\u003cp\u003eExisting treatments for anxiety include pharmacotherapy (PT) and cognitive‒behavioral therapy (CBT). However, certain anxiolytic drugs increase the risk of dementia, psychomotor disorders, pneumonia, and cancer (Weich et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Moreover, traditional CBT has limitations, with up to 36% of anxiety disorder patients not responding to it and as many as 40% of children and adolescents with anxiety disorders experiencing relapse after discontinuing CBT (Ginsburg et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Swain et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Therefore, exploring new and safe methods for alleviating anxiety is essential.\u003c/p\u003e\u003cp\u003eRecently, music therapy has emerged as a noninvasive treatment option with minimal side effects and a broad target population. Multiple studies have confirmed the clinical potential of music interventions for state anxiety. These studies demonstrate that music therapy can significantly relieve social anxiety disorders, depressive symptoms, and pain (Tang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Collectively, these findings suggest that music therapy has extensive potential applications in various clinical settings, warranting further promotion and implementation. However, recent studies lack sufficient focus on younger populations, particularly university students, and inadequately address the individual characteristics of music (Lu et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, the analysis of music's physical properties and the mechanisms of neural circuits remains limited (Nilsson, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA wide range of musical features have been studied in various research contexts. Bach's classical music is often used because of its consistent style and therapeutic effectiveness (Sharda et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Tonality plays a pivotal role in conveying emotions, with major and minor modes typically associated with feelings of happiness and sadness, respectively, due to their characteristic intervals (Balkwill \u0026amp; Thompson, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Nieminen et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The distinct timbres of the piano and violin also contribute to their differential effects on anxiety. The piano offers a balanced and wide harmonic spectrum, whereas the violin produces rich higher harmonics through bow friction (Sethares, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). On the basis of these findings, we hypothesize that a specific musical element may be more effective in influencing anxiety states.\u003c/p\u003e\u003cp\u003eNeurological research has confirmed that changes in the power density values of gamma and beta waves can serve as useful indicators of anxiety levels. Neuronal activity in the gamma frequency band increases when emotional and threat-related stimuli are processed (Keil et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; M\u0026uuml;ller et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Oya et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Furthermore, beta wave activity is closely related to emotional states such as anxiety, and a decrease in beta wave activity can reflect anxiety relief (Davidson, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The prefrontal electrodes (F3, F4) are widely used in studies on emotion regulation and anxiety-related brain activity (Vanhollebeke et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), while the central electrodes (C3, C4) have been employed in research on emotional regulation and cognitive function (Yang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These electrodes F3, F4, C3, and C4 also exhibit significant differences in beta and gamma power and have been used to investigate EEG spectral changes associated with anxiety and depression (Chen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Therefore, we consider the reduction in gamma and beta wave power spectral density on electrodes F3, F4, C3, C4 as an indicator of anxiety alleviation. The dorsal anterior cingulate cortex (dACC) and the dorsolateral prefrontal cortex (DLPFC) around these electrodes play significant roles in emotional regulation associated with fear and anxiety, involving the modulation of emotional conflict and the extinction of fear memories (Etkin et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Fox et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Therefore, we speculate that these brain regions may also be related to the neurological principles underlying music therapy.\u003c/p\u003e\u003cp\u003eThe current study aimed to investigate the impact of different musical instruments (piano and violin) and tonalities (major and minor) on anxiety relief in university students. EEG data of beta and gamma waves from the Prefrontal Cortex, which is channel F3, F4, C3 and C4, were collected for data analysis, with a music database being selected on the basis of Bach Werke Verzeichnis (BWV). The EEG data were further analyzed via wavelet transform and sLORETA brain localization. Moreover, the musical pieces underwent a spectrum analysis in terms of frequency density value and roughness. Our findings offer a new perspective on methods for alleviating anxiety and enhance our understanding of music therapy.\u003c/p\u003e"},{"header":"2. Methods and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Participants\u003c/h2\u003e\u003cp\u003e All participants provided written informed consent, which was approved by the Ethics Committee of Biology and Medicine at Northwestern Polytechnical University in China. All methods were performed in accordance with the relevant guidelines and regulations. The inclusion criteria for participants were recent experience of anxiety, absence of any other psychiatric disorders or familial psychiatric history, and no prior professional musical training. A total of 74 undergraduate and graduate students from Northwestern Polytechnical University in China were recruited and divided into two groups on the basis of their Self-Rating Anxiety Scale (SAS) score (Zung, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1971\u003c/span\u003e): an anxiety group (comprising 26 females and 12 males, with a mean age of 24.47 years and a standard deviation of 3.86 years) and a normal control group (consisting of 18 females and 12 males, with a mean age of 23.36 years and a standard deviation of 1.79 years) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All the participants were subsequently randomly assigned to one of four groups (PA, PI, VA, VI), where \"P\" represents the piano group, \"V\" signifies the violin group, \"A\" denotes the major key, and \"I\" indicates the minor key, to receive various musical interventions.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic data of participants recruited for the anxiety group and normal control group.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnxiety group (n\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNormal control (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.47 (3.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.36 (1.79)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale gender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale gender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSAS scores\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57.10 (8.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.4 (5.40)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Stimuli\u003c/h2\u003e\u003cp\u003eThe musical intervention materials utilized in the experiment included meticulously selected and edited instrumental music excerpts sourced from Bach's Works (BWV). As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, the entire selection and processing procedure encompassed six distinct stages: the establishment of a comprehensive music database, the categorization of musical materials, selection on the basis of tempo, the editing of segments, splicing and transitions, and final completion.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eInitially, pieces from Bach's keyboard works (BWV 772\u0026ndash;994) and chamber music compositions (BWV 1001\u0026ndash;1040) were chosen and compiled into a primary music database. The pieces in this database were subsequently categorized by instrument and tonality into four distinct groups (PA, PI, VA, VI), thereby forming a secondary database. From this secondary database, pieces with tempos ranging between 60 and 120 beats per minute (BPM) were selected, with five pieces from each group being chosen to establish a tertiary database.\u003c/p\u003e\u003cp\u003eDuring the segment editing phase, each selected piece underwent clipping to extract a coherent musical phrase, ideally spanning approximately 1\u0026ndash;2 minutes and encompassing a cadence. Following this, within each group, various musical segments were randomly spliced together, with the addition of transition effects at the commencement and conclusion of each track to create a cohesive and smooth listening experience. Ultimately, the chosen pieces were randomly ordered and spliced to ensure that the influence of any individual composition on the participants was minimized, thereby establishing the definitive final music database (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBeat-based Selected Music Library List and Random Splicing Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"+\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"+\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTitle\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePerformer\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTonality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBMV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eStart min\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eDuration (min)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eTempo(BPM/Hz)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eConnected (Major)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eConnected (Minor)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSonata No.1 in G minor BWV 1001# I- Adagio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eItzhak Perlman\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eviolin family\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eg minor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1001#1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1:39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"+\" colname=\"c10\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003e4\u0026thinsp;+\u0026thinsp;5\u0026thinsp;+\u0026thinsp;6\u0026thinsp;+\u0026thinsp;8\u0026thinsp;+\u0026thinsp;9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"+\" colname=\"c11\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003e1\u0026thinsp;+\u0026thinsp;2\u0026thinsp;+\u0026thinsp;3\u0026thinsp;+\u0026thinsp;7\u0026thinsp;+\u0026thinsp;10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePartita No.1 in B minor BWV 1002# I- Allemanda\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eItzhak Perlman\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eviolin family\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eb minor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1002#1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2:35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1:31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePartita No.2 in D minor BWV 1004#III- Sarabanda\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eItzhak Perlman\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eviolin family\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ed minor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1004#3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1:32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1:07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePartita No.3 in E BWV 1006#II- Loure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eItzhak Perlman\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eviolin family\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eE major\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1006#2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1:47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSonata No.3 in C BWV 1005#II- Fuga\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eItzhak Perlman\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eviolin family\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eC major\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1005#2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1:57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSonata No.3 in C BWV 1005#III- Largo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eItzhak Perlman\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eviolin family\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eC major\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1005#3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1:41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnaccompanied Cello Suite No. 2 in D minor, BWV 1008#2 - Pr\u0026eacute;lude\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMA Youyou\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eviolin family\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ed minor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1008#2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1:53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnaccompanied Cello Suite No. 1 in G Major, BWV 1007 - Sarabande\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMA Youyou\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eviolin family\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eG major\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1007#1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1:33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnaccompanied Cello Suite No. 6 in D Major, BWV 1012 - Allemande\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMA Youyou\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eviolin family\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eD major\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1012#1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1:44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnaccompanied Cello Suite No. 5 in C minor, BWV 1011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMA Youyou\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eviolin family\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ec minor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1011#1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1:28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLe Clavier bien temp\u0026eacute;r\u0026eacute; - Livre 2: Pr\u0026eacute;lude No.5 en R\u0026eacute; Majeur, BWV 874#5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZHU Xiaomei\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003epiano\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eD major\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e874#5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1:27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"+\" colname=\"c10\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003e1\u0026thinsp;+\u0026thinsp;3\u0026thinsp;+\u0026thinsp;5\u0026thinsp;+\u0026thinsp;6\u0026thinsp;+\u0026thinsp;9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"+\" colname=\"c11\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003e2\u0026thinsp;+\u0026thinsp;4\u0026thinsp;+\u0026thinsp;7\u0026thinsp;+\u0026thinsp;8\u0026thinsp;+\u0026thinsp;10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFugue No.8 in E-flat minor, BWV 853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZHU Xiaomei\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003epiano\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ee flat minor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e853#8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1:25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGoldberg Variations, BWV 988# Aria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZHU Xiaomei\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003epiano\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eG major\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1:03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2:05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePr\u0026eacute;lude N\u0026deg; 8 en Mi b\u0026eacute;mol mineur BWV 853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZHU Xiaomei\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003epiano\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ee flat minor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e853#8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1:31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFugue N\u0026deg; 23 en Si majeur BWV 868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZHU Xiaomei\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003epiano\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eB major\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e868#23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1:59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFugue N\u0026deg; 1 en Do majeur Bwv 846\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZHU Xiaomei\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003epiano\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eD major\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e846#1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1:50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFugue N\u0026deg; 4 en Do di\u0026egrave;se mineur Bwv 849\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZHU Xiaomei\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003epiano\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ec flat minor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e849#4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1:39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFugue N\u0026deg; 6 en R\u0026eacute; mineur Bwv 851\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZHU Xiaomei\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003epiano\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ed minor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e851#6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1:39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePr\u0026eacute;lude N\u0026deg; 7 en Mi b\u0026eacute;mol majeur Bwv 852\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZHU Xiaomei\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003epiano\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eE flat major\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e852#7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0:33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1:31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJohann Sebastian Bach - Pr\u0026eacute;lude N\u0026deg; 8 en Mi b\u0026eacute;mol mineur Bwv 853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZHU Xiaomei\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003epiano\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ee flat minor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e853#8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0:35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1:39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Experimental Design\u003c/h2\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1 STAI-S\u003c/h2\u003e\u003cp\u003eIn this study, the State Anxiety Subscale of the State-Trait Anxiety Inventory (STAI-S) was employed to explore the participants' subjective feelings and anxious state. Specifically, the STAI-S was utilized exclusively to assess and compare short-term anxiety states, without addressing the more enduring concept of \"trait anxiety.\"\u003c/p\u003e\u003cp\u003eThe subscale uses a 4-point Likert scale, where participants are required to rate the intensity of their feelings on a scale ranging from 1 (not at all) to 4 (very much so). The total score on the subscale can range from 20\u0026ndash;80, with higher scores indicating a greater level of state anxiety. A score of 40 or above is generally considered indicative of high anxiety levels.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2 Experimental process\u003c/h2\u003e\u003cp\u003e During the state anxiety intervention phase, including PA, PI, VA, and VI, participants were instructed to keep their phones on silent mode throughout the experiment to ensure a comfortable and uninterrupted experience. Furthermore, the experiment was conducted in a quiet, well-lit, and temperature-controlled (20\u0026ndash;22\u0026deg;C) music therapy room with the use of headphones.\u003c/p\u003e\u003cp\u003eThe entire experiment comprised five phases: an initial 5-minute resting period with the eyes closed, followed by an 8-minute state anxiety intervention, and a subsequent 5-minute resting period with the eyes closed. The participants completed the State Anxiety Subscale of the State-Trait Anxiety Inventory (STAI-S) before (BF) and after (AF) the experiment, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB. It was mandatory for all participants to complete the STAI-S for both BF and AF to accurately measure and compare their anxiety levels.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.4 EEG Data Acquisition\u003c/h2\u003e\u003cp\u003eEEG data were recorded from the subjects during both the resting state and the intervention state, with instructions to remain still throughout the recording process. The experimental data were collected via a 32-channel EEG system, model 8102, manufactured by Delica Medical Equipment Co., Ltd. in Shenzhen, China (Shenzhen Delica). Wet electrodes were utilized for the recording, with FCz serving as the reference electrode for online recording. The electrode cap layout adhered strictly to the international 10\u0026ndash;20 system standard, and the sampling rate was set at 1000 Hz. Throughout the experiment, the impedance of all electrodes was maintained below 50 kΩ to ensure the accuracy and reliability of the collected data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Data analysis\u003c/h2\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.5.1 Experimental Tracks\u003c/h2\u003e\u003cp\u003eTo ascertain the distinctions in spectral distribution between the timbre of a piano and a violin, the experimental audio was analyzed spectrographically via the fast Fourier transform (FFT) method, which specifically targeted the harmonic waveforms:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:X\\left[k\\right]=\\sum\\:_{n=0}^{N-1}\\:x\\left[n\\right]\\cdot\\:{e}^{-j\\frac{2\\pi\\:}{N}kn}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2.1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe fundamental frequency was determined by identifying the maximum values in the FFT results, and the magnitudes of the harmonics were subsequently calculated. To enhance clarity and comprehension of the results, the data were averaged for every 10 Hz frequency segment, yielding the average magnitude in dB. Ultimately, the spectral graphs were generated via Matplotlib.\u003c/p\u003e\u003cp\u003eThe roughness of each experimental audio piece was determined via MATLAB 9.13.0, where the audio signal was segmented into frames. The formula for calculating roughness, which is based on a psychoacoustic model proposed by Daniel and Weber (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), is provided below (Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e). In this formula, R represents the perceived roughness of the entire audio signal, measured with an asper. The term \u003cem\u003eg(z\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e​)\u003c/em\u003e is a weighted function related to the bark scale (Aures, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1985\u003c/span\u003e), which is an auditory-based frequency scale that accounts for the human ear's nonlinear perception of different frequencies.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:R=\\text{cal}\\sum\\:_{i=1}^{47}\\:{\\left(g\\left({z}_{i}\\right)\\cdot\\:{m}_{i}^{*}\\cdot\\:{k}_{i-2}\\cdot\\:{k}_{i}\\right)}^{2}\\text{[asper]}\\text{}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2.2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e2.5.2 Preprocessing EEG Data\u003c/h2\u003e\u003cp\u003eIn this study, the EEGLAB toolbox (Delorme \u0026amp; Makeig, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) implemented in MATLAB 9.13.0 was employed for preprocessing the resting-state EEG data collected during the experiment. A common average reference (CAR) method was applied, and a finite impulse response (FIR) filter was used to perform bandpass filtering between 0.5\u0026ndash;45 Hz and notch filtering between 48\u0026ndash;52 Hz to increase the signal-to-noise ratio. Additionally, the sampling rate was reduced to 500 Hz to increase computational efficiency and minimize noise impact (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eTo identify and remove ocular and muscular artifacts from the raw EEG signals, the ICLabel tool was utilized (Pion-Tonachini et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This tool, which is based on an artificial neural network (ANN) framework, automatically labels the source and likelihood of each independent component (IC). A threshold was established to remove ICs with an ocular or muscular artifact probability of 80% or higher. Using ICLabel's automatic function, all artifact components exceeding this threshold were excluded (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e2.5.3 EEG Data \u0026ndash; Spectral Density Value Analysis\u003c/h2\u003e\u003cp\u003eThe continuous wavelet transform (CWT) was employed to derive the absolute power spectral values across all channels for all the subjects (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). The wavelet basis function can be mathematically expressed as follows:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{\\psi\\:}_{a,b}\\left(t\\right)=\\frac{1}{\\sqrt{a}}\\psi\\:\\left(\\frac{t-b}{a}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2.3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the formula, a represents the scaling factor, b denotes the translation component, and t is the independent variable. The projection and decomposition of a continuous and finite energy signal \u003cem\u003ex(t)\u003c/em\u003e onto wavelet basis functions is defined as the CWT of the signal x(t), expressed as:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:W{T}_{x}(a,b)=⟨x\\left(t\\right),{\\psi\\:}_{a,b}\\left(t\\right)⟩=\\int\\:x\\left(t\\right){{\\psi\\:}_{ab}}^{*}\\left(t\\right)dt=\\frac{1}{\\sqrt{a}}{\\int\\:}_{-\\infty\\:}^{+\\infty\\:}\\:x\\left(t\\right){\\psi\\:}^{*}\\left(\\frac{t-b}{a}\\right)dt$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2.4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eDuring the S1 (Stable 1), T (Task), and S2 (Stable 2) periods, which were determined across the following frequency bands: beta (14\u0026ndash;30 Hz), and gamma (31\u0026ndash;44 Hz) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e2.5.4 EEG Data \u0026ndash; Brain Source Localization Analysis\u003c/h2\u003e\u003cp\u003eSLORETA-KEY is frequently used to accurately pinpoint signal origins in low-resolution brain imaging, ensuring that no localization errors occur (Pascual-Marqui et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In this study, this method was applied to investigate the specific spatial locations where beta and gamma waves undergo the most significant changes in the brain (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). The current version of the sLoreta software features a spatial resolution of 5x5x5 mm, corresponding to 6,239 voxels. Initially, the beta and gamma waves recorded before and after treatment were truncated to a 1-second segment at 150\u0026ndash;151 s. The dataset was subsequently further reduced by selecting 250 to 350 sampling points out of the total 500. The results were analyzed via paired sample t tests and nonparametric permutation tests for the P, V, A, and I groups.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e\u003cp\u003eOutliers in each dataset were identified and excluded based on Z-scores. To examine within-group differences before and after each intervention, paired t-tests were conducted on both the State-Trait Anxiety Inventory (STAI) scores and power spectral values (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). To compare changes in parameters across groups, a one-way analysis of variance (ANOVA) was performed on the percentage decreases in the P, V, A, and I components (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), in order to determine whether significant differences existed among groups. When significant effects were observed, pairwise comparisons between groups were conducted using unpaired t-tests.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePrior to conducting parametric analyses, the assumption of homogeneity of variances was assessed using Levene\u0026rsquo;s test. The normality of data distribution was evaluated using the Shapiro-Wilk test, which is particularly suitable for small sample sizes. For datasets that violated the assumption of normality, between-group comparisons were performed using the Kruskal-Wallis test. To control the family-wise error rate, the Bonferroni correction was applied to post hoc comparisons following parametric tests. For both parametric and non-parametric cases, Tukey\u0026rsquo;s HSD or Dunn\u0026rsquo;s test, respectively, was used for post hoc analysis. All statistical analyses were performed using GraphPad Prism (version 10.1.2), with statistical significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Experimental Music Significantly Reduces Anxiety\u003c/h2\u003e\u003cp\u003eTo preliminarily investigate whether selected music can alleviate anxiety, one-way ANOVA was conducted on the pre- and postexperimental STAI-S scores of both the anxiety and nonanxiety groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and B). As anticipated, anxiety scores demonstrated a decreasing trend across all groups, with a significant main effect observed for the music condition in both the anxiety group (F\u003csub\u003e(7,68)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;3.175, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0058) and the control group (F\u003csub\u003e(7,52)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;5.464, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). These results suggest that classical music, selected within a specific range of BPMs, has anxiety-reducing capabilities.\u003c/p\u003e\u003cp\u003eTo minimize the influence of human factors, we analyzed the spectral density values of beta and gamma brain waves via one-way ANOVA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC \u0026minus;\u0026thinsp;2F). For both brain waves, only in the control group did the music condition have a significant main effect on the target variable (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD: F\u003csub\u003e(7, 192)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.834, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0078; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF: F\u003csub\u003e(7, 257)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.209, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034). In the anxiety group, beta wave power significantly decreased in the PA, VA, and VI groups, whereas gamma wave power significantly decreased in the VI group, indicating reduced anxiety levels. Notably, the VA group presented the most substantial decrease (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and the VI group presented significant reductions in both the beta and gamma waves (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC \u0026minus;\u0026thinsp;2F).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Major Violin Music Significantly Alleviates Anxiety.\u003c/h2\u003e\u003cp\u003eTo delve deeper into whether a single factor was responsible for the significant changes observed in the two-factor (timbre and tonality) groups, we isolated the four elements P, V, A, and I (where P\u0026thinsp;=\u0026thinsp;PA\u0026thinsp;+\u0026thinsp;PI, V\u0026thinsp;=\u0026thinsp;VA\u0026thinsp;+\u0026thinsp;VI, A\u0026thinsp;=\u0026thinsp;PA\u0026thinsp;+\u0026thinsp;VA, and I\u0026thinsp;=\u0026thinsp;PI\u0026thinsp;+\u0026thinsp;VI) and conducted a statistical analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and B). Notably, in the Anxiety group, there was a significant preference for major music (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) and violin music (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0261). Conversely, in the control group, minor music (A: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0055; B: P\u0026thinsp;=\u0026thinsp;0.0041) and piano music (P\u0026thinsp;=\u0026thinsp;0.0306) led to a significant reduction in gamma waves (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u0026amp; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These findings suggest that major key violin music significantly reduces anxiety in individuals with high anxiety levels.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eChanges in Power Spectral Density of Separated Factors P, V, A, and I before and after music interventions in Beta Brain Waves and Gamma Brain Wave.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSample Size (N)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEffect Size (η\u0026sup2;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTest Power\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMean difference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eStandard Deviation (SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003et Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eDegrees of Freedom (df)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003ep Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBeta Wave\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eANXIETY-P\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2.669\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e4.701\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.6628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.5085\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eANXIETY-V\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eANXIETY-A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e-8.278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e3.777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2.191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003e0.03*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eANXIETY-I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCONTROL-P\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e4.868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e5.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.9605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.3386\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCONTROL-V\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCONTROL-A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e14.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e4.918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2.923\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003e0.0041*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCONTROL-I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGamma Wave\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eANXIETY-P\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e10.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e4.684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2.248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003e0.0261*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eANXIETY-V\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eANXIETY-A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.0246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e6.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.03887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.969\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eANXIETY-I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCONTROL-P\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e-12.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e5.833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2.186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003e0.0306*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCONTROL-V\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCONTROL-A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e5.515\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e3.445\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003e0.0008*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCONTROL-I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Violin Music Exhibits greater frequency density and roughness than does piano Music.\u003c/h2\u003e\u003cp\u003eTo gain a deeper understanding of how different musical elements influence anxiety levels, we employed the fast Fourier transform (FFT) to analyze the harmonic spectrum of each experimental track. Distinct waveform differences between the piano group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and B) and the violin group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC and D) were evident in the highlighted squares. To emphasize the significance of these differences, we conducted a statistical analysis of the roughness and power density of frequency between the two groups. Notably, the violin group presented significantly greater roughness and greater frequency power density than did the piano group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), suggesting that the acoustic characteristics of these two instruments in the high-frequency range are markedly distinct (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE and F).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Major Violin Music Activates a Broad Range of Cortical Regions in Emotion Regulation\u003c/h2\u003e\u003cp\u003eGiven the profound anxiety-reducing effects of major violin music, we conducted a source localization analysis to explore the spatial patterns underlying these changes. The sLORETA analysis revealed that, in the anxiety group, the violin elements primarily activated cortices associated with cognitive and emotional processing. In contrast, in the control group, the activated brain areas involved mainly the somatosensory function-related inferior parietal lobule and primary somatosensory cortex. Notably, both the anxiety group and the control group presented significant prefrontal cortex activity in response to piano music. Furthermore, in the anxiety group, minor elements distinctly activated the emotional cognitive cortex, whereas in the control group, major elements clearly activated the same region. Changes in the other groups were primarily associated with the somatosensory cortex. Similar patterns were observed in gamma wave activity, with the notable difference that major elements specifically activated the emotion-regulating cortex. Additionally, the gamma wave engaged a broader range of cortices than did the beta wave (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe Activated Brain Regions in Beta Wave and Gamma Wave Intervened by Different Musical Elements.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBroadman Area number\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSpecific Location\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBeta Wave\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eViolin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBA 9, 32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDorsolateral Prefrontal Cortex, Dorsal Anterior Cingulate Cortex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBA 40, 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSupramarginal Gyrus, Primary Somatosensory Cortex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePiano\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBA 9, 10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDorsolateral Prefrontal Cortex, Anterior Prefrontal Cortex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBA 9, 10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDorsolateral Prefrontal Cortex, Anterior Prefrontal Cortex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMajor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBA 7, 40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSuperior Parietal Lobule, Supramarginal Gyrus\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBA 7, 31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSuperior Parietal Lobule, Dorsal Posterior Cingulate Cortex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMinor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBA 47#, 11#\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInferior Frontal Gyrus, Orbitofrontal Cortex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBA 40, 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSupramarginal Gyrus, Primary Somatosensory Cortex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGamma Wave\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eViolin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBA 6#, 9#, 32#\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePremotor Cortex, Dorsolateral Prefrontal Cortex, Dorsal Anterior Cingulate Cortex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBA 23, 31, 10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVentral Posterior Cingulate Cortex, Dorsal Posterior Cingulate Cortex, Anterior Prefrontal Cortex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePiano\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBA 13#, 47#, 38#\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInsular Cortex, Inferior Frontal Gyrus, Temporal Pole\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBA 32#, 24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDorsal Anterior Cingulate Cortex, Ventral Anterior Cingulate Cortex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMajor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBA 32#, 24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDorsal Anterior Cingulate Cortex, Ventral Anterior Cingulate Cortex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBA 5, 7, 13#\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSomatosensory Association Cortex, Superior Parietal Lobule, Insular Cortex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMinor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBA 40, 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSupramarginal Gyrus\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBA 19#, 30*#\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVisual Association Cortex, Retrosplenial Cortex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e# indicates the right brain area. P\u0026thinsp;=\u0026thinsp;PA\u0026thinsp;+\u0026thinsp;PI; V\u0026thinsp;=\u0026thinsp;VA\u0026thinsp;+\u0026thinsp;VI; A\u0026thinsp;=\u0026thinsp;PA\u0026thinsp;+\u0026thinsp;VA; I\u0026thinsp;=\u0026thinsp;PI\u0026thinsp;+\u0026thinsp;VI. Abbreviations: BA, The Broadmann Area.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Major Violin Music Alleviates Anxiety by Activating the dACC and DLPFC.\u003c/h2\u003e\u003cp\u003eTo delve deeper into the neural mechanisms through which violin-based anxiety (VA) music alleviates anxiety, we utilized sLORETA analysis to examine the differences in current density following the VA music intervention. The gamma wave results demonstrated significant activation in the dorsal anterior cingulate cortex (dACC) and dorsolateral prefrontal cortex (DLPFC) (t\u0026thinsp;=\u0026thinsp;3.674, P\u0026thinsp;=\u0026thinsp;0.03540; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). In contrast, when piano-based intervention (PI) music was examined, no significant brain region activation was observed postintervention, with the most responsive areas centered around the visuomotor coordination region and the supramarginal gyrus implicated in phonology (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). These findings further imply that VA music may effectively mitigate anxiety by activating the dACC and DLPFC regions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eMusic therapy holds immense potential for alleviating anxiety, yet the physical attributes of music and the underlying neural circuits involved remain largely unknown. This study aimed to investigate the effects and mechanisms of music with varying emotions (major and minor) and instruments (piano and violin) on anxiety relief. A key innovative finding is that violin music, which is characterized by greater roughness and energy, is particularly effective in reducing anxiety.\u003c/p\u003e\u003cp\u003eOne major challenge in music therapy lies in the intrinsic complexity of music itself, which comprises multiple elements whose variability is difficult to control. We selected music by Bach, as Baroque compositions are known for their relatively uniform style and are less subject to the expressive fluctuations seen in other musical periods (Sharda et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, we constructed the experimental track by splicing together several ordinary musical phrases rather than using complete pieces, which was done to avoid the emotional progression, climax, and structural development that a full composition typically contains (Fedorenko et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Finally, the tempos to a specific BPM were constrained within the human resonance frequency range to minimize the physiological effects of rhythm on participants (MacDougall \u0026amp; Moore, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMusic therapy has shown promise across various clinical settings (Tang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To validate its anxiety-reducing effects, this study recorded participants' scale data and electroencephalogram (EEG) data before and after music interventions. Notably, all participants experienced significant anxiety relief following the interventions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), indicating that the selected music database generally possesses anxiety-reducing properties, which aligns with prior research (Barlas et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Weineck et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTonality, a fundamental aspect of music, plays a pivotal role in emotional communication (Balkwill \u0026amp; Thompson, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), with major and minor keys often associated with happiness and sadness, respectively. To assess the impact of musical emotions on anxiety, we conducted interventions using music with different tonalities on anxious individuals and analyzed the data via scale scores and EEG power spectral density. Our analysis revealed that individuals in control group preferred minor key music over major key music. Conversely, anxious individuals responded more favorably to major key music, experiencing a more pronounced anxiety-relieving effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Anxiety is a high-energy emotion; similarly, major key music is considered high-energy because of its capacity to evoke heightened arousal (Juslin \u0026amp; Laukka, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Punkanen et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ramirez \u0026amp; Vamvakousis, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Previous research by Yoon et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) revealed that low-energy emotions tend to favor low-energy music, and vice versa. Furthermore, high-energy, danceable music rich in lyrics has been shown to alleviate anxiety induced by pain (Howlin \u0026amp; Rooney, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hsieh et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), supporting our findings. We hypothesize that the control group may prefer minor key music because its low-energy characteristics induce calmness. In contrast, anxious individuals, driven by their high-energy emotional inertia, are more susceptible to distraction by high-energy music, thereby reducing anxiety.\u003c/p\u003e\u003cp\u003eTimbre stands as a pivotal characteristic in the realm of music. To delve deeper into how various timbres influence emotions, this study explored the impact of piano and violin music on anxiety relief. Intriguingly, the results revealed a discernible preference for musical instruments among the anxious cohort versus the control group. Specifically, violin music had a more potent anxiety-alleviating effect on the anxious group, whereas piano music was more efficacious for the control group, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB. The soothing effect of piano music on emotions and its stress-relieving capabilities have garnered widespread acknowledgment (Musa et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Toyoshima et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Conversely, the preference for violin music among anxious individuals presents an unexpected twist. To gain a clearer understanding of the factors driving these divergent outcomes, a spectral analysis of the experimental tracks was conducted. The findings revealed that violin music boasts a higher frequency density and greater roughness, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Given that roughness contributes to musical disharmony, our findings contradict traditional wisdom that harmonious music is superior in alleviating emotional stress (Bodner et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Štillov\u0026aacute; et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Notably, the music employed in this study was not \"dissonant\" but rather featured variations in timbre. We theorize that the less harmonious sound prompts cognitive interference, thereby shifting attention and enabling individuals to discharge internal tension. Mogg and Bradley (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) established that less harmonious music disrupts cognitive performance more than harmonious music does. In light of anxious individuals' tendency to exhibit heightened focus on specific stimuli, rough music may clash with their sensory information (McDermott et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Tenney, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), compelling them to redirect their attention to the music, thereby alleviating anxiety through distraction.\u003c/p\u003e\u003cp\u003eGiven the combined influence of beta and gamma wave alterations on the results, a deeper dive into the distinct effects of these wave types was undertaken. The sLORETA outcomes highlighted that, in the violin group, regions BA 9 and 32 exhibited increased activity among anxious participants (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), suggesting that violin music significantly impacts emotional and cognitive states, potentially mitigating anxiety symptoms. Conversely, the impact of violin music on the cognition of the population in control group was minimal, with notable effects observed in the somatosensory cortex. These findings reinforce the hypothesis that violin music serves as a more potent emotional intervention for anxious individuals. In the piano group, both anxious and normal participants demonstrated beta-wave activity within the dorsomedial prefrontal cortex (dmPFC) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Considering dmPFC dysregulation plays a pivotal role in anxiety pathogenesis (Rosenkranz et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), piano music may modulate anxiety across various emotional states through the dmPFC. An analysis of major and minor key elements revealed that anxious individuals exhibited notable fluctuations in brain regions associated with emotional regulation after exposure to minor key music (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). To ensure that beta wave behavior is not influenced by gamma waves, this observation must be integrated with a gamma wave analysis.\u003c/p\u003e\u003cp\u003eWhen analyzed in conjunction with gamma wave data, major key music had a more pronounced impact on emotional regulation than minor key music among individuals experiencing anxiety. The divergence in the results between gamma and beta waves could stem from the inhibitory influence of gamma waves on beta waves. Lundqvist et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) suggested that heightened gamma wave activity is commonly linked to the encoding or decoding of information, accompanied by a suppression of beta wave activity. Given the extensive reach of gamma waves across multiple Brodmann areas and their strong connections to advanced emotional processing and cognitive control regions, they may comprehensively mirror alterations in brain function during anxiety.\u003c/p\u003e\u003cp\u003eTo delve deeper into the neural mechanisms underlying the anxiety-alleviating effects of VA music, we conducted a brain source localization analysis postintervention with VA and PI music, with a focus on gamma brain waves. Notably, significant activation was observed in the dorsal anterior cingulate cortex (dACC) and dorsolateral prefrontal cortex (DLPFC) following the VA music intervention (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The dACC plays a pivotal role in the genesis of anxiety and fear, with its activation linked to overgeneralization in loss scenarios (Etkin et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). We propose that the heightened dACC activation during anxiety alleviation may represent an adaptive regulatory mechanism, potentially fostering adaptive responses. Moreover, the DLPFC is often hypoactive in depression, and its activation is intricately associated with antidepressant effects (Fox et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Given the neural overlap between anxiety and depression, the enhanced DLPFC activation following anxiety relief may underscore its vital role in emotional regulation. Furthermore, this augmented activation may inhibit the subgenual cingulate cortex, thereby mitigating anxiety (Fox et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In essence, VA music alleviates anxiety through the engagement of both the dACC and the DLPFC.\u003c/p\u003e"},{"header":"5. Limitations","content":"\u003cp\u003eSeveral limitations of the present study should be acknowledged. First, the absence of a true control group, which is no-treatment condition, restricts the interpretability of the results. While the study compared intervention groups with different levels of state anxiety, the lack of a non-intervention baseline makes it difficult to determine whether the observed effects were solely due to the interventions. As the state emotion changes with time, it would not be comparable to expose the same participant to both a music intervention and a non-intervention condition at different times, which highlights a broader methodological limitation in human experiments involving affective states.\u003c/p\u003e\u003cp\u003eSecond, while efforts were made to control various musical variables, such as BPM and the consistent compositional style of Bach, other expressive and acoustic features of the music remained difficult to standardize. Musical expressivity is complex and multi-dimensional by nature, and its impact on listeners can vary widely (Epp, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Wheeler, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This challenge is not unique to our study but reflects a broader limitation in music therapy research, where complete control over all auditory and emotional variables is inherently constrained.\u003c/p\u003e\u003cp\u003eFinally, for the participants recruitment, the sample size of each group was relatively small, which may have limited the statistical power of the analyses and the generalizability of the findings. Also, there was a gender imbalance in the participant pool, with a higher number of female participants. Given that men and women may respond differently to emotional and musical stimuli (Nater et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), this imbalance could have influenced the results. Future studies should aim for a more balanced gender distribution to enhance the representativeness and reliability of findings.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study offers novel insights into the role of timbre and tonality in anxiety relief. By comparing power spectral density values with various musical features, we found that violin music in a major key elicited the strongest responses. The neural mechanism underlying this effect likely involves the activation of the dorsal anterior cingulate cortex (dACC) and dorsolateral prefrontal cortex (DLPFC). Additionally, our findings suggest that gamma waves may be more appropriate than beta waves for studying high anxiety states. Our research sheds light on the relationship between musical elements and anxiety in individuals, providing a fresh perspective on anxiety alleviation techniques and deepening our understanding of how timbre and pitch can be harnessed to manage anxiety effectively.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received approval from the Ethics Committee of Biology and Medicine at Northwestern Polytechnical University. (Approval No: 202302054)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided written informed consent and were given detailed information regarding the study. Appropriate compensation was provided, and the confidentiality of personal data was strictly maintained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets utilized and examined in this study can be obtained from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest disclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest regarding the publication of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis publication was supported by Hong Kong Polytechnic University through the Undergraduate Research and Innovation Scheme (Project ID: P0047931) and the Fundamental Research Funds for the Central Universities\u0026nbsp;(Grant No. D5000230188).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQ. LUO was involved in conceptualization, methodology, software, formal analysis, investigation, writing, writing-review \u0026amp; editing, visualization, and funding acquisition. K. YANG contributed to methodology and software as well as formal analysis. Y. WANG participated in methodology, software, investigation, and writing. W. ZHANG, M. XI, A. QIAN and P. NIE provided resources. A. QIAN also took part in writing-review \u0026amp; editing and funding acquisition. G. CHEN was responsible for supervision. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAures, W. (1985). Berechnungsverfahren f\u0026uuml;r den sensorischen Wohlklang beliebiger Schallsignale. \u003cem\u003eActa Acustica united with Acustica\u003c/em\u003e,\u003cem\u003e 59\u003c/em\u003e(2), 130-141. \u003c/li\u003e\n\u003cli\u003eBalkwill, L.-L., \u0026amp; Thompson, W. F. (1999). A cross-cultural investigation of the perception of emotion in music: Psychophysical and cultural cues. \u003cem\u003eMusic perception\u003c/em\u003e,\u003cem\u003e 17\u003c/em\u003e(1), 43-64. \u003c/li\u003e\n\u003cli\u003eBarlas, T., Sodan, H. N., Avci, S., Cerit, E. T., \u0026amp; Yalcin, M. M. (2023). The impact of classical music on anxiety and pain perception during a thyroid fine needle aspiration biopsy. \u003cem\u003eHormones (Athens)\u003c/em\u003e,\u003cem\u003e 22\u003c/em\u003e(4), 581-585. https://doi.org/10.1007/s42000-023-00473-3 \u003c/li\u003e\n\u003cli\u003eBodner, E., Gilboa, A., \u0026amp; Amir, D. (2007). The unexpected side-effects of dissonance. \u003cem\u003ePsychology of Music\u003c/em\u003e,\u003cem\u003e 35\u003c/em\u003e(2), 286-305. \u003c/li\u003e\n\u003cli\u003eChen, H., Yang, J., Zhang, B., Zhang, L., Wang, J., Zhang, H., Zhang, H., Yu, C., Zhang, J., \u0026amp; Xu, Z. (2025). Quantitative analysis of the resting-state EEG power spectrum in patients with epilepsy comorbid with anxiety and depression. \u003cem\u003eActa Epileptol\u003c/em\u003e,\u003cem\u003e 7\u003c/em\u003e(1), 22. https://doi.org/10.1186/s42494-025-00206-6 \u003c/li\u003e\n\u003cli\u003eDaniel, P., \u0026amp; Weber, R. (1997). Psychoacoustical roughness: Implementation of an optimized model. \u003cem\u003eActa Acustica united with Acustica\u003c/em\u003e,\u003cem\u003e 83\u003c/em\u003e(1), 113-123. \u003c/li\u003e\n\u003cli\u003eDavidson, R. J. (2000). Affective style, psychopathology, and resilience: brain mechanisms and plasticity. \u003cem\u003eAmerican psychologist\u003c/em\u003e,\u003cem\u003e 55\u003c/em\u003e(11), 1196. \u003c/li\u003e\n\u003cli\u003eDelorme, A., \u0026amp; Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. \u003cem\u003eJ Neurosci Methods\u003c/em\u003e,\u003cem\u003e 134\u003c/em\u003e(1), 9-21. https://doi.org/10.1016/j.jneumeth.2003.10.009 \u003c/li\u003e\n\u003cli\u003eEndler, N. S., \u0026amp; Kocovski, N. L. (2001). State and trait anxiety revisited. \u003cem\u003eJournal of anxiety disorders\u003c/em\u003e,\u003cem\u003e 15\u003c/em\u003e(3), 231-245. \u003c/li\u003e\n\u003cli\u003eEpp, E. (2007). Locating the autonomous voice: Self-expression in music-centered music therapy. Voices: A world forum for music therapy, \u003c/li\u003e\n\u003cli\u003eEtkin, A., Egner, T., \u0026amp; Kalisch, R. (2011). Emotional processing in anterior cingulate and medial prefrontal cortex. \u003cem\u003eTrends Cogn Sci\u003c/em\u003e,\u003cem\u003e 15\u003c/em\u003e(2), 85-93. https://doi.org/10.1016/j.tics.2010.11.004 \u003c/li\u003e\n\u003cli\u003eFedorenko, E., McDermott, J. H., Norman-Haignere, S., \u0026amp; Kanwisher, N. (2012). Sensitivity to musical structure in the human brain. \u003cem\u003eJournal of neurophysiology\u003c/em\u003e,\u003cem\u003e 108\u003c/em\u003e(12), 3289-3300. \u003c/li\u003e\n\u003cli\u003eFox, M. D., Buckner, R. L., White, M. P., Greicius, M. D., \u0026amp; Pascual-Leone, A. (2012). Efficacy of transcranial magnetic stimulation targets for depression is related to intrinsic functional connectivity with the subgenual cingulate. \u003cem\u003eBiol Psychiatry\u003c/em\u003e,\u003cem\u003e 72\u003c/em\u003e(7), 595-603. https://doi.org/10.1016/j.biopsych.2012.04.028 \u003c/li\u003e\n\u003cli\u003eGinsburg, G. S., Becker, E. M., Keeton, C. P., Sakolsky, D., Piacentini, J., Albano, A. M., Compton, S. N., Iyengar, S., Sullivan, K., \u0026amp; Caporino, N. (2014). Naturalistic follow-up of youths treated for pediatric anxiety disorders. \u003cem\u003eJAMA psychiatry\u003c/em\u003e,\u003cem\u003e 71\u003c/em\u003e(3), 310-318. \u003c/li\u003e\n\u003cli\u003eHarder, H. G., Wagner, S., \u0026amp; Rash, J. (2016). \u003cem\u003eMental illness in the workplace: Psychological disability management\u003c/em\u003e. Routledge. \u003c/li\u003e\n\u003cli\u003eHowlin, C., \u0026amp; Rooney, B. (2021). Patients choose music with high energy, danceability, and lyrics in analgesic music listening interventions. \u003cem\u003ePsychology of Music\u003c/em\u003e,\u003cem\u003e 49\u003c/em\u003e(4), 931-944. \u003c/li\u003e\n\u003cli\u003eHsieh, C., Kong, J., Kirsch, I., Edwards, R. R., Jensen, K. B., Kaptchuk, T. J., \u0026amp; Gollub, R. L. (2014). Well-loved music robustly relieves pain: a randomized, controlled trial. \u003cem\u003ePLoS One\u003c/em\u003e,\u003cem\u003e 9\u003c/em\u003e(9), e107390. https://doi.org/10.1371/journal.pone.0107390 \u003c/li\u003e\n\u003cli\u003eJuslin, P. N., \u0026amp; Laukka, P. (2004). Expression, perception, and induction of musical emotions: A review and a questionnaire study of everyday listening. \u003cem\u003eJournal of new music research\u003c/em\u003e,\u003cem\u003e 33\u003c/em\u003e(3), 217-238. \u003c/li\u003e\n\u003cli\u003eKeil, A., M\u0026uuml;ller, M. M., Gruber, T., Wienbruch, C., Stolarova, M., \u0026amp; Elbert, T. (2001). Effects of emotional arousal in the cerebral hemispheres: a study of oscillatory brain activity and event-related potentials. \u003cem\u003eClin Neurophysiol\u003c/em\u003e,\u003cem\u003e 112\u003c/em\u003e(11), 2057-2068. https://doi.org/10.1016/s1388-2457(01)00654-x \u003c/li\u003e\n\u003cli\u003eKocsis, R. N. (2013). Book Review: Diagnostic and Statistical Manual of Mental Disorders: Fifth Edition (DSM-5). \u003cem\u003eInternational Journal of Offender Therapy and Comparative Criminology\u003c/em\u003e,\u003cem\u003e 57\u003c/em\u003e(12), 1546-1548. https://doi.org/10.1177/0306624x13511040 \u003c/li\u003e\n\u003cli\u003eLu, G., Jia, R., Liang, D., Yu, J., Wu, Z., \u0026amp; Chen, C. (2021). Effects of music therapy on anxiety: A meta-analysis of randomized controlled trials. \u003cem\u003ePsychiatry research\u003c/em\u003e,\u003cem\u003e 304\u003c/em\u003e, 114137. \u003c/li\u003e\n\u003cli\u003eLundqvist, M., Rose, J., Herman, P., Brincat, S. L., Buschman, T. J., \u0026amp; Miller, E. K. (2016). Gamma and Beta Bursts Underlie Working Memory. \u003cem\u003eNeuron\u003c/em\u003e,\u003cem\u003e 90\u003c/em\u003e(1), 152-164. https://doi.org/10.1016/j.neuron.2016.02.028 \u003c/li\u003e\n\u003cli\u003eMacDougall, H. G., \u0026amp; Moore, S. T. (2005). Marching to the beat of the same drummer: the spontaneous tempo of human locomotion. \u003cem\u003eJournal of applied physiology\u003c/em\u003e,\u003cem\u003e 99\u003c/em\u003e(3), 1164-1173. \u003c/li\u003e\n\u003cli\u003eMcDermott, J. H., Lehr, A. J., \u0026amp; Oxenham, A. J. (2010). Individual differences reveal the basis of consonance. \u003cem\u003eCurr Biol\u003c/em\u003e,\u003cem\u003e 20\u003c/em\u003e(11), 1035-1041. https://doi.org/10.1016/j.cub.2010.04.019 \u003c/li\u003e\n\u003cli\u003eMogg, K., \u0026amp; Bradley, B. P. (1998). A cognitive-motivational analysis of anxiety. \u003cem\u003eBehaviour research and therapy\u003c/em\u003e,\u003cem\u003e 36\u003c/em\u003e(9), 809-848. \u003c/li\u003e\n\u003cli\u003eM\u0026uuml;ller, M. M., Keil, A., Gruber, T., \u0026amp; Elbert, T. (1999). Processing of affective pictures modulates right-hemispheric gamma band EEG activity. \u003cem\u003eClin Neurophysiol\u003c/em\u003e,\u003cem\u003e 110\u003c/em\u003e(11), 1913-1920. https://doi.org/10.1016/s1388-2457(99)00151-0 \u003c/li\u003e\n\u003cli\u003eMusa, A., Ng, Q. X., Wai, Y. Z., \u0026amp; Iqbal, T. (2022). Effect of slow tempo music on markers of anxiety during cataract surgery: Randomized control trial. \u003cem\u003eTaiwan J Ophthalmol\u003c/em\u003e,\u003cem\u003e 12\u003c/em\u003e(1), 74-81. https://doi.org/10.4103/tjo.tjo_10_21 \u003c/li\u003e\n\u003cli\u003eNater, U. M., Abbruzzese, E., Krebs, M., \u0026amp; Ehlert, U. (2006). Sex differences in emotional and psychophysiological responses to musical stimuli. \u003cem\u003eInt J Psychophysiol\u003c/em\u003e,\u003cem\u003e 62\u003c/em\u003e(2), 300-308. https://doi.org/10.1016/j.ijpsycho.2006.05.011 \u003c/li\u003e\n\u003cli\u003eNieminen, S., Ist\u0026oacute;k, E., Brattico, E., \u0026amp; Tervaniemi, M. (2012). The development of the aesthetic experience of music: Preference, emotions, and beauty. \u003cem\u003eMusicae Scientiae\u003c/em\u003e,\u003cem\u003e 16\u003c/em\u003e(3), 372-391. \u003c/li\u003e\n\u003cli\u003eNilsson, U. (2008). The anxiety-and pain-reducing effects of music interventions: a systematic review. \u003cem\u003eAORN journal\u003c/em\u003e,\u003cem\u003e 87\u003c/em\u003e(4), 780-807. \u003c/li\u003e\n\u003cli\u003eOya, H., Kawasaki, H., Howard, M. A., 3rd, \u0026amp; Adolphs, R. (2002). Electrophysiological responses in the human amygdala discriminate emotion categories of complex visual stimuli. \u003cem\u003eJ Neurosci\u003c/em\u003e,\u003cem\u003e 22\u003c/em\u003e(21), 9502-9512. https://doi.org/10.1523/jneurosci.22-21-09502.2002 \u003c/li\u003e\n\u003cli\u003ePascual-Marqui, R. D., Faber, P., Kinoshita, T., Kochi, K., Milz, P., Keiichiro, N., \u0026amp; Yoshimura, M. (2018). A comparison of bivariate frequency domain measures of electrophysiological connectivity. \u003cem\u003ebioRxiv\u003c/em\u003e, 459503. \u003c/li\u003e\n\u003cli\u003ePion-Tonachini, L., Kreutz-Delgado, K., \u0026amp; Makeig, S. (2019). ICLabel: An automated electroencephalographic independent component classifier, dataset, and website. \u003cem\u003eNeuroimage\u003c/em\u003e,\u003cem\u003e 198\u003c/em\u003e, 181-197. https://doi.org/10.1016/j.neuroimage.2019.05.026 \u003c/li\u003e\n\u003cli\u003ePunkanen, M., Eerola, T., \u0026amp; Erkkil\u0026auml;, J. (2011). Biased emotional preferences in depression: Decreased liking of angry and energetic music by depressed patients. \u003cem\u003eMusic and Medicine\u003c/em\u003e. \u003c/li\u003e\n\u003cli\u003eRamirez, R., \u0026amp; Vamvakousis, Z. (2012). Detecting emotion from EEG signals using the emotive epoc device. International Conference on Brain Informatics, \u003c/li\u003e\n\u003cli\u003eRosenkranz, J. A., Moore, H., \u0026amp; Grace, A. A. (2003). The prefrontal cortex regulates lateral amygdala neuronal plasticity and responses to previously conditioned stimuli. \u003cem\u003eJ Neurosci\u003c/em\u003e,\u003cem\u003e 23\u003c/em\u003e(35), 11054-11064. https://doi.org/10.1523/jneurosci.23-35-11054.2003 \u003c/li\u003e\n\u003cli\u003eSethares, W. A. (2005). \u003cem\u003eTuning, timbre, spectrum, scale\u003c/em\u003e. Springer Science \u0026amp; Business Media. \u003c/li\u003e\n\u003cli\u003eSharda, N., Mattoon, E., Matters, L., Prewitt, J., McDonald, S., Sloane, R., Cassas, C., \u0026amp; White, H. (2019). Bach to the Basics: Implementation and Impact of a Postoperative, Inpatient Personalized Music Program for Older Adults. \u003cem\u003eJ Perianesth Nurs\u003c/em\u003e,\u003cem\u003e 34\u003c/em\u003e(2), 347-353. https://doi.org/10.1016/j.jopan.2018.05.006 \u003c/li\u003e\n\u003cli\u003e\u0026Scaron;tillov\u0026aacute;, K., Kiska, T., Koriť\u0026aacute;kov\u0026aacute;, E., Str\u0026yacute;ček, O., Mekyska, J., Chrastina, J., \u0026amp; Rektor, I. (2021). Mozart effect in epilepsy: Why is Mozart better than Haydn? Acoustic qualities-based analysis of stereoelectroencephalography. \u003cem\u003eEur J Neurol\u003c/em\u003e,\u003cem\u003e 28\u003c/em\u003e(5), 1463-1469. https://doi.org/10.1111/ene.14758 \u003c/li\u003e\n\u003cli\u003eSwain, J., Hancock, K., Hainsworth, C., \u0026amp; Bowman, J. (2013). Acceptance and commitment therapy in the treatment of anxiety: a systematic review. \u003cem\u003eClinical psychology review\u003c/em\u003e,\u003cem\u003e 33\u003c/em\u003e(8), 965-978. \u003c/li\u003e\n\u003cli\u003eSzuhany, K. L., \u0026amp; Simon, N. M. (2022). Anxiety Disorders: A Review. \u003cem\u003eJAMA\u003c/em\u003e,\u003cem\u003e 328\u003c/em\u003e(24), 2431-2445. https://doi.org/10.1001/jama.2022.22744 \u003c/li\u003e\n\u003cli\u003eTang, H., Chen, L., Wang, Y., Zhang, Y., Yang, N., \u0026amp; Yang, N. (2021). The efficacy of music therapy to relieve pain, anxiety, and promote sleep quality, in patients with small cell lung cancer receiving platinum-based chemotherapy. \u003cem\u003eSupportive care in cancer\u003c/em\u003e,\u003cem\u003e 29\u003c/em\u003e(12), 7299-7306. \u003c/li\u003e\n\u003cli\u003eTenney, J. (1988). A history of consonance and dissonance. \u003c/li\u003e\n\u003cli\u003eToyoshima, K., Fukui, H., \u0026amp; Kuda, K. (2011). Piano playing reduces stress more than other creative art activities. \u003cem\u003eInternational Journal of Music Education\u003c/em\u003e,\u003cem\u003e 29\u003c/em\u003e(3), 257-263. \u003c/li\u003e\n\u003cli\u003eVanhollebeke, G., De Smet, S., De Raedt, R., Baeken, C., van Mierlo, P., \u0026amp; Vanderhasselt, M. A. (2022). The neural correlates of psychosocial stress: A systematic review and meta-analysis of spectral analysis EEG studies. \u003cem\u003eNeurobiol Stress\u003c/em\u003e,\u003cem\u003e 18\u003c/em\u003e, 100452. https://doi.org/10.1016/j.ynstr.2022.100452 \u003c/li\u003e\n\u003cli\u003eWeich, S., Pearce, H. L., Croft, P., Singh, S., Crome, I., Bashford, J., \u0026amp; Frisher, M. (2014). Effect of anxiolytic and hypnotic drug prescriptions on mortality hazards: retrospective cohort study. \u003cem\u003eBmj\u003c/em\u003e,\u003cem\u003e 348\u003c/em\u003e. \u003c/li\u003e\n\u003cli\u003eWeineck, K., Wen, O. X., \u0026amp; Henry, M. J. (2022). Neural synchronization is strongest to the spectral flux of slow music and depends on familiarity and beat salience. \u003cem\u003eElife\u003c/em\u003e,\u003cem\u003e 11\u003c/em\u003e. https://doi.org/10.7554/eLife.75515 \u003c/li\u003e\n\u003cli\u003eWheeler, B. L. (2016). 720Music Therapy Research: An Overview. In J. Edwards (Ed.), \u003cem\u003eThe Oxford Handbook of Music Therapy\u003c/em\u003e (pp. 0). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199639755.013.11 \u003c/li\u003e\n\u003cli\u003eYang, Y., Zhang, X., Zhu, Y., Dai, Y., Liu, T., \u0026amp; Wang, Y. (2015). Cognitive impairment in generalized anxiety disorder revealed by event-related potential N270. \u003cem\u003eNeuropsychiatr Dis Treat\u003c/em\u003e,\u003cem\u003e 11\u003c/em\u003e, 1405-1411. https://doi.org/10.2147/ndt.S84666 \u003c/li\u003e\n\u003cli\u003eYoon, S., Verona, E., Schlauch, R., Schneider, S., \u0026amp; Rottenberg, J. (2020). Why do depressed people prefer sad music? \u003cem\u003eEmotion\u003c/em\u003e,\u003cem\u003e 20\u003c/em\u003e(4), 613-624. https://doi.org/10.1037/emo0000573 \u003c/li\u003e\n\u003cli\u003eZhu, X., Lazarov, A., Dolan, S., Bar-Haim, Y., Dillon, D. G., Pizzagalli, D. A., \u0026amp; Schneier, F. (2023). Resting state connectivity predictors of symptom change during gaze-contingent music reward therapy of social anxiety disorder. \u003cem\u003ePsychological medicine\u003c/em\u003e,\u003cem\u003e 53\u003c/em\u003e(7), 3115-3123. \u003c/li\u003e\n\u003cli\u003eZung, W. W. (1971). A rating instrument for anxiety disorders. \u003cem\u003ePsychosomatics: Journal of Consultation and Liaison Psychiatry\u003c/em\u003e. \u003c/li\u003e\n\u003c/ol\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Anxiety, Music therapy, Violin, Major key, EEG, sLORETA, dACC, DLPFC, Gamma waves","lastPublishedDoi":"10.21203/rs.3.rs-6879305/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6879305/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUniversity students may encounter high levels of anxiety or depression, which can adversely impact their daily lives and academic performance and even lead to illness. Recently, studies have reported that music therapy can effectively alleviate anxiety, although the underlying mechanisms remain unclear. In this study, we assessed therapeutic outcomes and explored several cognitive mechanisms of \"major keys\", \"minor keys\", \"violin music\" and \"piano music\" for individuals experiencing university-related anxiety. Our assessments included power spectral density values of beta and gamma brain waves, as well as quantitative evaluations of musical pieces and brain localization experiences. The results indicated that anxious participants exposed to major key piano music experienced a significant reduction in anxiety levels compared with those in the minor key piano music group. The brain localization results further suggested that gamma waves are more suitable than beta waves for highly anxious participants. The possible neural circuits involved may include the activation of the dorsomedial prefrontal cortex (dACC) and the left dorsolateral prefrontal cortex (DLPFC). Our findings revealed that violin music in the major key can effectively relieve anxiety through the activation of the dACC and DLPFC circuits.\u003c/p\u003e","manuscriptTitle":"Violin Major Music Alleviates University Students’ Anxiety Maybe through the dACC and DLPFC Circuits","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 13:01:52","doi":"10.21203/rs.3.rs-6879305/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-25T02:55:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-21T09:19:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-19T20:05:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145400309327834634426667635554450023267","date":"2025-08-18T02:12:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-11T15:02:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184646941592486303750057959975188290682","date":"2025-08-06T18:01:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27126218859167463001386056431209470391","date":"2025-08-04T09:37:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"120143872823971268610334548081626068551","date":"2025-08-02T06:47:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332507800019674892730778922801480387004","date":"2025-08-01T15:45:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-01T14:50:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-07T13:52:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-30T16:43:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-27T09:06:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-06-12T10:11:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"87a9ad96-a6fd-4eaf-969c-c67f76fb3843","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":52988662,"name":"Biological sciences/Neuroscience/Emotion/Prefrontal cortex"},{"id":52988663,"name":"Biological sciences/Neuroscience/Neural circuit"},{"id":52988664,"name":"Biological sciences/Psychology/Human behaviour"}],"tags":[],"updatedAt":"2025-12-08T16:08:19+00:00","versionOfRecord":{"articleIdentity":"rs-6879305","link":"https://doi.org/10.1038/s41598-025-31161-4","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-06 15:57:36","publishedOnDateReadable":"December 6th, 2025"},"versionCreatedAt":"2025-08-12 13:01:52","video":"","vorDoi":"10.1038/s41598-025-31161-4","vorDoiUrl":"https://doi.org/10.1038/s41598-025-31161-4","workflowStages":[]},"version":"v1","identity":"rs-6879305","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6879305","identity":"rs-6879305","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}