Accelerometer-based characteristics of evoked mechanomyograms in the orbicularis oculi muscle

Accelerometer-based characteristics of evoked mechanomyograms in the orbicularis oculi muscle | 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 Research Article Accelerometer-based characteristics of evoked mechanomyograms in the orbicularis oculi muscle Yasushi Itoh, Kumi Akataki, Chihiro Momoi, Koji Inui, Katsumi Mita This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8130145/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Feb, 2026 Read the published version in Experimental Brain Research → Version 1 posted You are reading this latest preprint version Abstract Introduction/Aims Electromyograms (EMGs) of the orbicularis oculi muscle are commonly used to assess the blink reflex, which provides clinically important information. However, mechanomyograms (MMGs) may serve as a promising alternative. This study aimed to evaluate the utility of MMGs in assessing orbicularis oculi responses to stimulation. Methods Electrical stimulation was applied to the facial nerve axons of 25 healthy adult participants, and MMGs were recorded using an accelerometer. The reliability of amplitude-based parameters ( A max and A p−p ) and time-based parameters ( T rise , T zero , T fall , and T rec ) characterizing MMG waveforms was assessed using the intraclass correlation coefficient (ICC), standard error of measurement (SEM), and coefficient of variation (CV). Additionally, the amplitude and temporal characteristics of MMGs evoked by varying stimulus intensities were compared with simultaneously recorded EMGs. Results All parameters, except T rec , showed high relative reliability (95% confidence interval of ICC of > 0.75) and high absolute reliability (%SEM and CV < 10%). MMG amplitudes ( A max and A p−p ) and EMG amplitudes ( V max and V p−p ) increased linearly with stimulus intensity. A strong correlation was found between the amplitudes of both signals ( V max vs. A max : r = 0.932, p < 0.0001; V p−p vs. A p−p : r = 0.937, p < 0.0001). The first positive MMG peak consistently appeared at a fixed interval (5.52 ± 2.10 ms) after the EMG peak, irrespective of stimulus level. Discussion These findings suggest that MMG amplitude and latency (stimulus-to-peak time) are reliable indicators of orbicularis oculi muscle activity and can be effectively used alongside EMG in clinical and research settings. accelerometer mechanomyogram orbicularis oculi muscle reliability stimulus response Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction The orbicularis oculi muscle is an essential structure frequently selected for evaluation in clinical practice. For instance, it is employed in assessing the normality of reflex pathways in patients with schizophrenia via the prepulse inhibition response of the eyeblink reflex (Edwards et al. 2008 ; Meincke et al. 1999 ; Taiminen et al. 2000 ), as well as in the diagnosis and monitoring of facial palsy (Kanaya et al. 2009 ; On et al. 2007 ; Takahashi et al. 2017 ) and ocular myasthenia gravis (Matsuoka and Katsuda 1968 ; Oey et al. 1993 ; Valls-Canals et al. 2003 ) through electrodiagnostic techniques such as electroneurography and single-fiber electromyography. The active state of the orbicularis oculi is often identified using electromyograms (EMGs) as a key indicator. Recently, Bayasgalan and colleagues (Bayasgalan et al. 2024 ) reported that mechanomyograms (MMGs) could serve as a promising alternative for evaluating the blink reflex. MMGs offer several advantages over EMGs, including faster and simpler recordings and the absence of artifacts from electrical stimulation. MMGs are noninvasive recordings of surface vibrations generated by the contractile activity of muscle fibers and contain sufficient data to assess skeletal muscle activity (Orizio 2004 ). While EMG reflects the electrical aspect of muscle contraction, MMG captures its mechanical counterpart (Orizio 1993 ; Stokes and Cooper 1992 ). Moreover, MMG is known to reveal information not discernible from surface EMG—such as changes at the motor unit level (Orizio 2004 )—and its combination with EMG can provide insights into muscle electromechanical efficiency (Cè et al. 2017 ). Due to its mode of generation, MMG is particularly advantageous for evaluating muscles that operate without joints between their origin and insertion. Nonetheless, studies on MMG of the orbicularis oculi muscle remain limited. Various types of transducers have been used to detect skin surface microvibrations resulting from muscle fiber contraction, including piezoelectric contact sensors (Barry 1991 ; Orizio et al. 1994 ), condenser microphones (Stokes and Dalton 1991 ; Watakabe et al. 2001 ), accelerometers (Akataki et al. 1999 ; Orizio et al. 1996 ; Watakabe et al. 2003 ), and digital displacement transducers (Paravlić et al. 2017 ; Tous-Fajardo et al. 2010 ). Signals recorded by these different transducer types may reflect distinct physical quantities (e.g., displacement, velocity, or acceleration) (Orizio 2004 ; Watakabe et al. 2001 , 2003 ). However, signals obtained using accelerometers can be directly compared across studies (Orizio 2004 ), because, unlike other transducers, they are expressed in standard physical units (m s − 2 ) rather than in device-dependent units. Additionally, accelerometers are lightweight and compact and can detect surface microvibrations simply by being attached to the skin with double-sided tape, making them especially suitable for examining small muscles. The aim of this study was to evaluate the usefulness of accelerometer-derived MMG for assessing electrically evoked activity of the orbicularis oculi muscle. To achieve this, the relative and absolute reliabilities of MMGs elicited by electrical stimulation of facial nerve axons directly innervating the orbicularis oculi were investigated. Changes in MMG responses with varying stimulation intensities were also compared to those of EMGs. 2. Methods 2.1 Participants A total of 25 healthy adults (20 males and 5 females; age, 20.1 ± 0.6 years; height, 169.5 ± 6.2 cm; body mass, 63.6 ± 11.4 kg; mean ± standard deviation) with no history of ophthalmological or neuromuscular disorders participated in this study. The study adhered to the principles of the Declaration of Helsinki. The Ethics Committee of Osaka Electro-Communication University approved the experimental protocol (Approval number: 22 − 013; Approval date: 1 March 2023). All participants were fully informed of the approved procedures and provided written informed consent prior to the experiment. 2.2 Experimental design All measurements were conducted with participants in the right semi-lateral decubitus position. To account for MMG changes influenced by skin temperature (Mito et al. 2007 ), the room temperature was maintained between 24°C and 26°C during all measurements. Sessions involving repetitive supramaximal stimulation for reliability assessment and those using varying stimulation intensities to compare MMG and EMG were performed on separate days. To minimize post-activation depression and fatigue, a rest interval of at least 10 s was implemented between successive stimuli in each session (Palmieri et al. 2004 ; Paravlić et al. 2017 ). 2.3 Stimulation protocol To elicit orbicularis oculi twitches, electrical stimulation was delivered to the facial nerve through the participant’s left stylomastoid foramen near the temporal bone using a surface-stimulating electrode (NM-420S, Nihon Kohden Corporation, Tokyo, Japan). The stimulation signal consisted of a single square wave with a 0.2 ms duration, generated by an electromyography/evoked potential measuring system (MEB-2200, Nihon Kohden Corporation, Tokyo, Japan). The repetitive supramaximal stimulation session was conducted with all participants. First, the maximum stimulus intensity for each participant was determined as the current at which the evoked EMG amplitude no longer increased with further increments in stimulus intensity. Subsequently, twitch contractions of the orbicularis oculi muscle were elicited 10 times per participant at 120% of the maximum stimulus intensity. The intensity-varying stimulation session was conducted with 20 participants who were available for additional testing. First, a single twitch contraction at the maximum stimulus intensity (100% level) was elicited. Then, electrical stimulation at various intensities ranging from 120% to 30% was applied once at each level. The stimulus intensity step was approximately 3 mA. 2.4 Recording MMG was recorded using a single-axis compact accelerometer (MP110-10-101, a rectangular solid; width, 9 mm; depth, 9 mm; height, 5 mm; 0.8 g; Medi Sens Inc., Tokyo, Japan) attached to the left lower eyelid using double-sided tape (Fig. 1 ). Simultaneously, surface EMG was recorded using two disposable Ag/AgCl gel electrodes (NM-31, Nihon Kohden Corporation, Tokyo, Japan) placed on the left lower eyelid and the nasion. Electrode placement was determined based on the location where surface EMG, evoked at the same stimulation intensity, showed the highest amplitude (Takahashi et al. 2017 ). The MMG and EMG signals were amplified and filtered using their respective amplifiers (MPS110, Medi Sens Inc., Tokyo, Japan; bandpass filter: 0.1–1,000 Hz; MEB-2200, Nihon Kohden Corporation, Tokyo, Japan; bandpass filter: 0.1–2,000 Hz). The signals were then digitized and stored on a personal computer via an A/D converter data acquisition system (ML880, ADInstruments, Dunedin, New Zealand) at a sampling frequency of 10 kHz. 2.5 Analysis 2.5.1 Extracting parameters Following a previous study (Than et al. 2018 ), two amplitude-based parameters ( A max , A p−p ) and four time-based parameters ( T rise , T zero , T fall , T rec ) characterizing the MMG waveform were extracted (Fig. 2 ). From the EMG recordings, the peak amplitude of the positive wave ( V max ) and the peak-to-peak amplitude ( V p−p ) were extracted. Additionally, the time lag ( T del ) between the first positive peaks of the MMG and EMG was calculated. 2.5.2 Statistical analysis As an index of absolute reliability, the percentage of the standard error of measurement to the grand mean (%SEM) was calculated using the following Eq. ( 1 ) (de Vet et al. 2006 ; Paravlić et al. 2017 ): $$\:\text{%}\text{S}\text{E}\text{M}=\frac{\sqrt{\text{W}\text{M}\text{S}}}{\text{G}\text{r}\text{a}\text{n}\text{d}\:\text{m}\text{e}\text{a}\text{n}}\times\:100$$ 1 , where WMS denotes the within-participant mean squares from one-way analysis of variance. Another index, the coefficient of variation (CV), was calculated as the mean of individual coefficients of variation, defined as the standard deviation of repeated measurements for each participant divided by the mean and multiplied by 100 (Atkinson et al. 1998). To evaluate the relative reliability of the observed data, the intraclass correlation coefficient based on a one-way random effects model, single-measurement type—ICC(1,1), was calculated using the following Eq. ( 2 ) (Gwet 2008 ; Koo and Li 2016 ): $$\:\text{I}\text{C}\text{C}\left(\text{1,1}\right)=\frac{\text{B}\text{M}\text{S}\:-\:\text{W}\text{M}\text{S}}{\text{B}\text{M}\text{S}\:+\:\left(K\:-\:1\right)\:\text{W}\text{M}\text{S}}$$ 2 , where BMS represents the between-participant mean squares from a one-way analysis of variance, and K is the number of repeated measurements per participant. Since relative reliability can be improved by using the mean of repeated measurements, the intraclass correlation coefficient based on the one-way random effects model, mean-measurement type—ICC(1, k )—was also calculated using the following Eq. ( 3 ): $$\:\text{I}\text{C}\text{C}\left(1,\text{k}\right)=\frac{\text{B}\text{M}\text{S}\:-\:\text{W}\text{M}\text{S}}{\text{B}\text{M}\text{S}}$$ 3 . Here, the mean data was computed from k observations ( k = 2,3,..., K ), randomly selected from among the K repeated measurements. These ICC estimates were reported with 95% confidence intervals (CIs), and their reliability levels were interpreted according to standard guidelines: values of 0.9 indicate poor, moderate, good, and excellent reliability, respectively (Koo and Li 2016 ). To evaluate correlations between any two parameters, both Spearman’s rank correlation coefficient ( r s ) and Pearson’s correlation coefficient ( r ) were calculated. All statistical analyses were conducted using STATA 11.1 (StataCorp LLC., Texas, USA). 2.5.3 Spectral analysis The power spectrum of the MMG signal was calculated using a fast Fourier transform (FFT) algorithm. The analysis window (data length = 0.2048 s, 2048 points) was defined by extracting the segment from − 0.8 ms (i.e., 0.8 ms before the stimulus) to 50.4 ms of the MMG recording. A Tukey window (window width = 0.0512 s, 512 points) was applied, and zero was assigned to the remaining data points. The mean power frequency (MPF) was then calculated from the 0–200 Hz frequency band of the resulting spectrum. 3. Results As shown in Fig. 2 , the MMG waveform of the orbicularis oculi muscle evoked by supramaximal intensity stimulation consistently exhibited an initial positive wave with the highest peak amplitude, followed by a negative wave in all participants. However, the characteristics of the decaying oscillations following the negative wave varied among individuals. The results of the reliability indices for each parameter characterizing the MMG waveform are summarized in Table 1 . In terms of absolute reliability, %SEM values were below 9% for all parameters except for T rec , which exceeded 10% substantially. The CVs were ≤ 6.20% for all parameters except T rec , where they also exceeded 10%. Regarding relative reliability, the 95% CIs of ICC(1,1) for the amplitude-derived parameters ( A max and A p−p ) clearly surpassed 0.90. For the time-derived parameters, the 95% CIs of ICC(1,1) ranged from 0.75 to 1.00 for T rise , T zero , and T fall , whereas the CI for T rec was below 0.75. However, the 95% CIs of ICC(1, k ) for T rec increased with the number of averaged trials ( k ), exceeding 0.75 when k > 5 (Fig. 3 ). Table 1 Relative and absolute reliabilities of each parameter extracted from the evoked mechanomyogram ( n = 25) Parameter (Units) Grand mean ± SD ICC(1,1) (95% CI) %SEM (%) CV (%) A max (m s − 2 ) 6.01 ± 1.65 0.973 (0.955–0.986) 4.63 3.89 A p−p (m s − 2 ) 10.18 ± 2.80 0.973 (0.954–0.986) 4.69 3.80 T rise (ms) 3.25 ± 0.72 0.928 (0.883–0.962) 6.26 5.33 T zero (ms) 5.13 ± 1.07 0.862 (0.784–0.926) 8.47 6.20 T fall (ms) 9.87 ± 2.26 0.904 (0.846–0.949) 7.57 5.38 T rec (ms) 4.63 ± 1.83 0.574 (0.428–0.734) 33.59 12.62 ICC(1,1) (95% CI)- intraclass correlation coefficient estimate with 95% confidence interval based on single measurements; %SEM- standard error of measurement divided by the grand mean and multiplied by 100; CV- coefficient of variation Representative EMG and MMG recordings of the orbicularis oculi muscle at various stimulus intensities are shown in Fig. 4 a, and 4 d. Both EMG and MMG peak amplitudes increased with stimulus intensity, while waveform shapes remained relatively stable. Changes in the normalized peak amplitudes of EMG and MMG with varying stimulus levels are shown in Fig. 4 (b, c, e, f). In the submaximal stimulus intensity range, there was a strong correlation between stimulus intensity and MMG amplitudes ( A max , r = 0.851, r s = 0.860, p < 0.0001; A p−p , r = 0.851, r s = 0.861, p < 0.0001). Similarly, EMG amplitudes were highly correlated with stimulus level ( V max , r = 0.900, r s = 0.905, p < 0.0001; V p−p , r = 0.901, r s = 0.906, p < 0.0001). Additionally, the normalized amplitudes of EMG and MMG showed very high correlations in this range ( A max vs. V max , r = 0.932, r s = 0.930, p < 0.0001; A p−p vs. V p−p , r = 0.937, r s = 0.933, p < 0.0001) (Fig. 5 ). As shown in Fig. 6 (a), the total and peak power spectra estimated from the MMG recordings in Fig. 4 (d) were highest at the 100% stimulus level (thick line) and decreased progressively with lower stimulus levels. All spectra displayed a unimodal shape, with more than 80% of the total power distributed between 5 Hz and 70 Hz, and the upper limit of the frequency components reaching no more than 100 Hz. Additionally, spectral shape and bandwidth showed minimal change across stimulus intensities. These trends were consistent across all participants. For all participants, the MPF showed no statistically significant correlation with stimulus intensity and remained approximately constant at around 33 Hz (Fig. 6 (b)). Normalized MPF also did not correlate significantly with stimulus level, although greater variability was observed below the 70% stimulus threshold (Fig. 6 (c)). The first positive peak of the MMG waveform consistently appeared later than that of the EMG in all participants. The time delay of MMG relative to EMG ( T del ) remained nearly constant across stimulus levels (5.52 ± 2.10 ms) (Fig. 7 (a)). Similarly, normalized T del showed no significant correlation with stimulus level, though its variability increased at lower stimulus intensities (Fig. 7 (b)). 4. Discussion The evoked MMGs of the orbicularis oculi muscle exhibited waveforms resembling underdamped oscillations, beginning with a positive wave of maximum amplitude (Fig. 2 ). This pattern is consistent with previously reported MMG waveforms of limb muscles during twitch contractions (Bichler 2000 ; Orizio et al. 1999 ). According to Bichler ( 2000 ), the initial and largest amplitude fluctuations in MMG recordings occur during the contraction phase of a twitch and are associated with force generation in the muscle fibers. The report also noted that the slower oscillations observed during the subsequent relaxation phase—when twitch tension decreases—were attributed to the characteristics of the transducer (a piezoelectric microphone) used in those measurements. However, that study employed paraffin oil to fill the space between the microphone and the muscle surface, which differs from our method, where accelerometers were directly attached to the skin over the muscle. Because accelerometers detect object acceleration, our recordings likely reflect the fluctuating acceleration of the skin surface. It is generally understood that the twitch tension behavior during the relaxation phase is governed by the passive mechanical properties of muscle. In mathematical models of skeletal muscle, tension decay during relaxation is represented by a viscoelastic element composed of muscle fibers and surrounding connective tissue (Lemos et al. 2008 ; Phillips et al. 2004 ). Given the relationship between geometric changes in muscle fibers and vibrations on the skin surface, the oscillations observed after the negative wave in accelerometer-recorded signals are likely to reflect these passive viscoelastic properties of both the muscle and the skin—rather than being artifacts of the transducer itself. This study examined the reliability of six parameters characterizing the initial oscillations of the evoked MMG of the orbicularis oculi muscle under supramaximal stimulation. According to previous reports (Cè et al. 2013 ; Ng et al. 2017 ), acceptable thresholds for reliability are %SEM and CV values below 10%. Based on their criteria, the %SEM and CV results suggest that all parameters—except T rec —demonstrate sufficient absolute reliability. The ICC(1,1) values indicate that all parameters except T rec were rated as excellent or good to excellent according to established guidelines, reflecting high relative reliability. The lower reliability of T rec may be attributed to the fact that, among the six parameters, only T rec is associated with the relaxation phase of the twitch contraction. However, as shown in Fig. 3 , T rec also achieves sufficiently high relative reliability (Good to Excellent) when averaged over five or more repeated measurements. EMG amplitude has frequently been used to assess the intensity of the orbicularis oculi muscle response (Unal et al. 2016 ; Valls-Solé et al. 1997 ). The amplitude of evoked EMG is generally known to increase proportionally with stimulus intensity, and our results confirmed this linear relationship in the orbicularis oculi muscle up to the 100% stimulus level (Fig. 4 (b, c)). Conversely, based on known MMG characteristics, the rate of increase in evoked MMG amplitude is expected to change if the type of recruited muscle fiber shifts with increasing stimulus intensity. Specifically, MMG amplitude generated by slow-twitch (ST) fibers is lower than that from fast-twitch (FT) fibers, meaning that the amplitude increase per additional active fiber is also small (Marchetti et al. 1992 ). During twitch contractions elicited by motor nerve stimulation, FT fibers are recruited first—contrary to the “size principle”—followed by ST fibers as stimulus intensity rises (Baratta et al. 1989 ). Consequently, the rate of MMG amplitude increase might be expected to slow beyond a certain stimulus level. However, the present results show that, similar to EMG, MMG amplitude also increases linearly with stimulus intensity (Fig. 4 (e, f)). In addition, a strong correlation between EMG and MMG amplitudes was observed (Fig. 5 ). These findings suggest that the peak amplitude of MMG, like EMG, can serve as a reliable indicator of the response intensity of the orbicularis oculi muscle. The linear increase in MMG amplitude may be attributed to the muscle’s structural characteristics. According to previous anatomical studies (Hwang et al. 2011 ; Johnson et al. 1973 ), FT fibers constitute more than 80–90% of the orbicularis oculi muscle, which could account for the observed linearity in MMG response. MMG is known to reflect the activity of muscle fibers located closer to the surface more strongly than that of deeper fibers (Orizio 2004 ). As a result, the activity of ST fibers—which are fewer in number and typically located deeper within the muscle—may have little to no influence on the evoked MMG of the orbicularis oculi. This interpretation is supported by our spectral analysis results (Fig. 6 ). Specifically, since MMG signals from FT fibers are known to contain higher frequency components than those from ST fibers (Orizio 1993 ), any change in the type of active muscle fibers should be reflected in the spectral shape, bandwidth, or mean frequency. However, our findings showed no changes in these spectral characteristics across different stimulus levels, suggesting no apparent influence of ST fiber recruitment on the MMG. Our results showed that the first positive peak of the MMG occurred clearly later than that of the EMG (Fig. 7 ). The delay time ( T del ) remained nearly constant at approximately 5.5 ms, regardless of stimulus level. According to a previous study (Sun et al. 1996 ), there is a delay of about 10 ms between the action potential and the onset of force development due to the excitation-contraction coupling process. MMGs recorded with laser displacement meters—which reflect skin displacement—have been reported to precede twitch tension due to differences in the propagation pathway (Orizio et al. 1999 ). From a mathematical perspective, skin acceleration is expected to reach its maximum shortly after the onset of skin displacement. Therefore, the consistent time lag ( T del ) between MMG and EMG, independent of stimulus intensity and averaging about 5.5 ms, may be directly related to the muscle’s force generation process. Traditionally, the time from stimulus to the peak EMG response is used as a latency marker in reflex pathway assessments (Unal et al. 2016 ; Valls-Solé et al. 1997 ). Thus, our findings suggest that the time from stimulus to the peak MMG response can also serve as a suitable indicator for reflex pathway evaluation, despite differences in absolute values. Moreover, analysis of T del may provide additional insights into changes in the muscle force generation process. 5. Conclusion The mechanomyographic twitch response of the orbicularis oculi muscle, as detected by accelerometry, demonstrated sufficiently high absolute and relative reliability. Furthermore, the amplitude and latency of the MMG’s first positive peak—similar to conventional EMG—were shown to be valid indicators for assessing the intensity of the orbicularis oculi muscle response and the integrity of reflex pathways. Declarations Competing Interests The authors declare no competing interests. Ethics Approval The study complies with the principles laid down in the Declaration of Helsinki. The Ethics Committee of Osaka Electro-Communication University approved the experimental protocol (Approval number: 22 − 013, Approval date: 1 March 2023). Consent to Participate Informed consent was obtained from all individual participants included in the study. Funding This research was supported by Grants-in-Aid for Scientific Research (Grant number 23K07026 to K.I.). Author Contribution Conceptualization: Y.I. and K.A.; Methodology: Y.I., K.A., and K.M.; Formal analysis and investigation: Y.I. and C.M.; Writing – original draft preparation: Y.I.; Writing– review & editing: K.A. and K.I.; Funding acquisition: K.I.; Resources: K.A. and K.I.; Supervision: K.M.; Final version of the manuscript: all authors. Acknowledgement The authors would like to thank all the participants involved in the study for their patience and committed involvement. The authors would also like to thank Enago (www.enago.jp) for the English language review. We gratefully acknowledge the support provided by Osaka Electro-Communication University for this study. Data Availability The datasets generated and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request. 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J Athl Train 39:268–277 Paravlić A, Zubac D, Šimunič B (2017) Reliability of the twitch evoked skeletal muscle electromechanical efficiency: a ratio between tensiomyogram and M-wave amplitudes. J Electromyogr Kinesiol 37:108–116. https://doi.org/10.1016/j.jelekin.2017.10.002 Phillips CA, Repperger DW, Neidhard-Doll AT, Reynolds DB (2004) Biomimetic model of skeletal muscle isometric contraction: I. an energetic-viscoelastic model for the skeletal muscle isometric force twitch. Comput Biol Med 34:307–322. https://doi.org/10.1016/S0010-4825(03)00061-1 Stokes MJ, Cooper RG (1992) Muscle sounds during voluntary and stimulated contractions of the human adductor pollicis muscle. J Appl Physiol 72:1908–1913. https://doi.org/10.1152/jappl.1992.72.5.1908 Stokes MJ, Dalton PA (1991) Acoustic myography for investigating human skeletal muscle fatigue. 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Schizophr Res 44(1):69–79. https://doi.org/10.1016/s0920-9964(99)00140-1 Than C, Seidl L, Tosovic D, Brown JM (2018) Test-retest reliability of laser displacement mechanomyography in paraspinal muscles while in lumbar extension or flexion. J Electromyogr Kinesiol 41:60–65. https://doi.org/10.1016/j.jelekin.2018.05.001 Tous-Fajardo J, Moras G, Rodríguez-Jiménez S, Usach R, Doutres DM, Maffiuletti NA (2010) Inter-rater reliability of muscle contractile property measurements using non-invasive tensiomyography. J Electromyogr Kinesiol 20:761–766. https://doi.org/10.1016/j.jelekin.2010.02.008 Unal Z, Domac FM, Boylu E, Kocer A, Tanridag T, Us O (2016) Blink reflex in migraine headache. North Clin Istanb 3(1):1–8. https://doi.org/10.14744/nci.2016.30301 Valls-Canals J, Povedano M, Montero J, Pradas J (2003) Stimulated single-fiber EMG of the frontalis and orbicularis oculi muscles in ocular myasthenia gravis. Muscle Nerve 28:501–503. https://doi.org/10.1002/mus.10426 Valls-Solé J, Valldeoriola F, Tolosa E, Marti MJ MJ (1997) Distinctive abnormalities of facial reflexes in patients with progressive supranuclear palsy. Brain 120(10):1877–1883. https://doi.org/10.1093/brain/120.10.1877 Watakabe M, Mita K, Akataki K, Itoh Y (2001) Mechanical behaviour of condenser microphone in mechanomyography. Med Biol Eng Comput 39:195–201. https://doi.org/10.1007/BF02344804 Watakabe M, Mita K, Akataki K, Ito K (2003) Reliability of the mechanomyogram detected with an accelerometer during voluntary contractions. Med Biol Eng Comput 41:198–202. https://doi.org/10.1007/BF02344888 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8130145","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":548863948,"identity":"0264953f-6c6d-4cb5-ad2a-4a951f9dd680","order_by":0,"name":"Yasushi 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06:58:53","extension":"xml","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":118821,"visible":true,"origin":"","legend":"","description":"","filename":"307ed8b095e44def9a094c9d9e489b991structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8130145/v1/66de99e8885091d536504bf7.xml"},{"id":96968195,"identity":"13cbf60e-acc9-4b9a-a8df-879b83a488c1","added_by":"auto","created_at":"2025-11-28 06:58:54","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":130812,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8130145/v1/0e79c2f645e1d3ad5dc17761.html"},{"id":97136849,"identity":"4329c434-d376-4d07-ab98-a70a8781c6a9","added_by":"auto","created_at":"2025-12-01 09:57:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":87297,"visible":true,"origin":"","legend":"\u003cp\u003ePositioning of mechanomyogram and electromyogram sensors for recordings of the orbicularis oculi twitch response\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8130145/v1/073b63404d95dddd95e246a9.png"},{"id":96968178,"identity":"8beb5428-81d8-4bd2-b332-a58e036a6df8","added_by":"auto","created_at":"2025-11-28 06:58:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5576,"visible":true,"origin":"","legend":"\u003cp\u003eTwo typical evoked mechanomyogram waveforms of the orbicularis oculi with descriptions of extracted parameters.\u003cstrong\u003e \u003c/strong\u003eTypical waveforms from different participants are shown in (a) and (b), respectively. These curves are characterized by acceleration-based (\u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003ep-p\u003c/em\u003e\u003c/sub\u003e) and temporal (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003erise\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003ezero\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003efall\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003erec\u003c/em\u003e\u003c/sub\u003e) parameters. \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e is defined as the amplitude of the first positive peak, and \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003emin\u003c/em\u003e\u003c/sub\u003e as the subsequent negative peak. \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003ep-p\u003c/em\u003e\u003c/sub\u003e is calculated as \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e minus \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003emin\u003c/em\u003e\u003c/sub\u003e. \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003erise\u003c/em\u003e\u003c/sub\u003e is the time interval between 10% and 90% of A\u003csub\u003emax\u003c/sub\u003e on the initial ascending slope. \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003ezero\u003c/em\u003e\u003c/sub\u003e is the interval between 90% of A\u003csub\u003emax\u003c/sub\u003e and the subsequent zero crossing on the descending slope. \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003efall\u003c/em\u003e\u003c/sub\u003e is defined as the time from 90% of \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e to 90% of \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003emin\u003c/em\u003e\u003c/sub\u003e. \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003erec\u003c/em\u003e\u003c/sub\u003e is the interval from 90% to 50% of \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003emin\u003c/em\u003e\u003c/sub\u003e on the ascending slope following the negative peak. Bold arrows indicate the timing of stimulation\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8130145/v1/9860ccd1eeb122e4113278bc.png"},{"id":96968177,"identity":"7254bacc-3f37-4be0-95b3-87f8de88ea4e","added_by":"auto","created_at":"2025-11-28 06:58:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2418,"visible":true,"origin":"","legend":"\u003cp\u003eThe intra-class correlation coefficient ICC (1, \u003cem\u003ek\u003c/em\u003e) of \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003erec\u003c/em\u003e\u003c/sub\u003e as a function of the number of averaged measurements (\u003cem\u003ek\u003c/em\u003e).\u003cstrong\u003e \u003c/strong\u003eFilled circles represent the calculated ICC(1,\u003cem\u003ek\u003c/em\u003e) values based on the mean-measurement type, and error bars show the corresponding 95% confidence intervals\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8130145/v1/e3b1d82ee6d8ad1827a7218e.png"},{"id":96968180,"identity":"5cde1cc6-08b7-4554-8fc8-59ad2846a6f1","added_by":"auto","created_at":"2025-11-28 06:58:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":20148,"visible":true,"origin":"","legend":"\u003cp\u003eTypical electromyogram and mechanomyogram waveforms at various stimulation levels, and the relationships between stimulus level and maximum or peak-to-peak amplitude for each signal. The waveforms of the electromyogram (a) and mechanomyogram (d) are shown with one thick solid line representing the 100% stimulus level and six thin solid lines representing 90%, 80%, 70%, 60%, 50%, and 40% stimulus levels, in descending order of peak amplitude. Electrical stimulation was applied at 0 s. Sharp spikes, seen across all EMG recordings, are artifacts of the electrical stimulation. Panels (b), (c), (e), and (f) show plots of\u003cem\u003e V\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003ep-p\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003ep-p\u003c/em\u003e\u003c/sub\u003e, respectively, for all participants in relation to stimulus level. Stimulus level is defined as the applied stimulus intensity divided by the maximum stimulus intensity and multiplied by 100. The vertical axis of each plot is normalized by dividing by the reference amplitude at the 100% stimulus level. Each graph includes a regression line (solid line) fitted to the data below the 100%, along with its corresponding equation\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8130145/v1/294ef3b47611267f65dfd845.png"},{"id":97137628,"identity":"22686e4e-fe73-4eec-9751-c4703f94e552","added_by":"auto","created_at":"2025-12-01 09:58:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":6948,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between electromyogram and mechanomyogram amplitudes. Each plot shows the relationship between \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e (a), and between \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003ep-p\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003ep-p\u003c/em\u003e\u003c/sub\u003e (b). Both axes in each plot are normalized by dividing by the respective reference value at the 100% stimulus level\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8130145/v1/1e1bb2d8ed71ef636c0c9bd0.png"},{"id":96968176,"identity":"6f534c78-2410-4ef4-8dbd-21df62e64613","added_by":"auto","created_at":"2025-11-28 06:58:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":8119,"visible":true,"origin":"","legend":"\u003cp\u003ePower spectrum and mean power frequency of mechanomyograms at various stimulus levels. In (a), a set of representative power spectra is shown. The thick line corresponds to the 100% stimulus level, while the six thin lines represent spectra at 90%, 80%, 70%, 60%, 50%, and 40% stimulus levels, in order of decreasing peak value. Panels (b) and (c) show the changes in mean power frequency (MPF) and normalized MPF, respectively, in relation to stimulus level for all participants. Normalized MPF was calculated for each participant as the difference from the reference MPF at the 100% stimulus level\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8130145/v1/17f337cbc2da32c0fcf1574a.png"},{"id":96968183,"identity":"30f12a3e-b563-4923-a2db-b61dfd350671","added_by":"auto","created_at":"2025-11-28 06:58:53","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":4487,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the time lag between electromyogram and mechanomyogram with stimulation level.\u003cstrong\u003e \u003c/strong\u003ePanels (a) and (b) show the changes in \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003edel\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e \u003c/em\u003eand normalized \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003edel\u003c/em\u003e\u003c/sub\u003e, respectively, as a function of stimulus level. Normalized \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003edel\u003c/em\u003e\u003c/sub\u003e was calculated as the difference from the reference \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003edel\u003c/em\u003e\u003c/sub\u003e at the 100% stimulus level\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-8130145/v1/28be7e80c22ca5022126650b.png"},{"id":103251480,"identity":"fc1533ed-6296-4a2e-a5cb-c2b67395c8ca","added_by":"auto","created_at":"2026-02-23 16:09:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":913692,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8130145/v1/435014ce-ef51-410a-9618-87c75f111ae3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Accelerometer-based characteristics of evoked mechanomyograms in the orbicularis oculi muscle","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe orbicularis oculi muscle is an essential structure frequently selected for evaluation in clinical practice. For instance, it is employed in assessing the normality of reflex pathways in patients with schizophrenia via the prepulse inhibition response of the eyeblink reflex (Edwards et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Meincke et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Taiminen et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), as well as in the diagnosis and monitoring of facial palsy (Kanaya et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; On et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Takahashi et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and ocular myasthenia gravis (Matsuoka and Katsuda \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1968\u003c/span\u003e; Oey et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Valls-Canals et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) through electrodiagnostic techniques such as electroneurography and single-fiber electromyography. The active state of the orbicularis oculi is often identified using electromyograms (EMGs) as a key indicator. Recently, Bayasgalan and colleagues (Bayasgalan et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported that mechanomyograms (MMGs) could serve as a promising alternative for evaluating the blink reflex. MMGs offer several advantages over EMGs, including faster and simpler recordings and the absence of artifacts from electrical stimulation.\u003c/p\u003e\u003cp\u003eMMGs are noninvasive recordings of surface vibrations generated by the contractile activity of muscle fibers and contain sufficient data to assess skeletal muscle activity (Orizio \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). While EMG reflects the electrical aspect of muscle contraction, MMG captures its mechanical counterpart (Orizio \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Stokes and Cooper \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Moreover, MMG is known to reveal information not discernible from surface EMG\u0026mdash;such as changes at the motor unit level (Orizio \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)\u0026mdash;and its combination with EMG can provide insights into muscle electromechanical efficiency (C\u0026egrave; et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Due to its mode of generation, MMG is particularly advantageous for evaluating muscles that operate without joints between their origin and insertion. Nonetheless, studies on MMG of the orbicularis oculi muscle remain limited.\u003c/p\u003e\u003cp\u003eVarious types of transducers have been used to detect skin surface microvibrations resulting from muscle fiber contraction, including piezoelectric contact sensors (Barry \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Orizio et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), condenser microphones (Stokes and Dalton \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Watakabe et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), accelerometers (Akataki et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Orizio et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Watakabe et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), and digital displacement transducers (Paravlić et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tous-Fajardo et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Signals recorded by these different transducer types may reflect distinct physical quantities (e.g., displacement, velocity, or acceleration) (Orizio \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Watakabe et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2001\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). However, signals obtained using accelerometers can be directly compared across studies (Orizio \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), because, unlike other transducers, they are expressed in standard physical units (m s\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) rather than in device-dependent units. Additionally, accelerometers are lightweight and compact and can detect surface microvibrations simply by being attached to the skin with double-sided tape, making them especially suitable for examining small muscles.\u003c/p\u003e\u003cp\u003eThe aim of this study was to evaluate the usefulness of accelerometer-derived MMG for assessing electrically evoked activity of the orbicularis oculi muscle. To achieve this, the relative and absolute reliabilities of MMGs elicited by electrical stimulation of facial nerve axons directly innervating the orbicularis oculi were investigated. Changes in MMG responses with varying stimulation intensities were also compared to those of EMGs.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Participants\u003c/h2\u003e\u003cp\u003eA total of 25 healthy adults (20 males and 5 females; age, 20.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 years; height, 169.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2 cm; body mass, 63.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4 kg; mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation) with no history of ophthalmological or neuromuscular disorders participated in this study. The study adhered to the principles of the Declaration of Helsinki. The Ethics Committee of Osaka Electro-Communication University approved the experimental protocol (Approval number: 22\u0026thinsp;\u0026minus;\u0026thinsp;013; Approval date: 1 March 2023). All participants were fully informed of the approved procedures and provided written informed consent prior to the experiment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Experimental design\u003c/h2\u003e\u003cp\u003eAll measurements were conducted with participants in the right semi-lateral decubitus position. To account for MMG changes influenced by skin temperature (Mito et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), the room temperature was maintained between 24\u0026deg;C and 26\u0026deg;C during all measurements. Sessions involving repetitive supramaximal stimulation for reliability assessment and those using varying stimulation intensities to compare MMG and EMG were performed on separate days. To minimize post-activation depression and fatigue, a rest interval of at least 10 s was implemented between successive stimuli in each session (Palmieri et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Paravlić et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Stimulation protocol\u003c/h2\u003e\u003cp\u003eTo elicit orbicularis oculi twitches, electrical stimulation was delivered to the facial nerve through the participant\u0026rsquo;s left stylomastoid foramen near the temporal bone using a surface-stimulating electrode (NM-420S, Nihon Kohden Corporation, Tokyo, Japan). The stimulation signal consisted of a single square wave with a 0.2 ms duration, generated by an electromyography/evoked potential measuring system (MEB-2200, Nihon Kohden Corporation, Tokyo, Japan).\u003c/p\u003e\u003cp\u003e The repetitive supramaximal stimulation session was conducted with all participants. First, the maximum stimulus intensity for each participant was determined as the current at which the evoked EMG amplitude no longer increased with further increments in stimulus intensity. Subsequently, twitch contractions of the orbicularis oculi muscle were elicited 10 times per participant at 120% of the maximum stimulus intensity.\u003c/p\u003e\u003cp\u003eThe intensity-varying stimulation session was conducted with 20 participants who were available for additional testing. First, a single twitch contraction at the maximum stimulus intensity (100% level) was elicited. Then, electrical stimulation at various intensities ranging from 120% to 30% was applied once at each level. The stimulus intensity step was approximately 3 mA.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Recording\u003c/h2\u003e\u003cp\u003eMMG was recorded using a single-axis compact accelerometer (MP110-10-101, a rectangular solid; width, 9 mm; depth, 9 mm; height, 5 mm; 0.8 g; Medi Sens Inc., Tokyo, Japan) attached to the left lower eyelid using double-sided tape (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Simultaneously, surface EMG was recorded using two disposable Ag/AgCl gel electrodes (NM-31, Nihon Kohden Corporation, Tokyo, Japan) placed on the left lower eyelid and the nasion. Electrode placement was determined based on the location where surface EMG, evoked at the same stimulation intensity, showed the highest amplitude (Takahashi et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The MMG and EMG signals were amplified and filtered using their respective amplifiers (MPS110, Medi Sens Inc., Tokyo, Japan; bandpass filter: 0.1\u0026ndash;1,000 Hz; MEB-2200, Nihon Kohden Corporation, Tokyo, Japan; bandpass filter: 0.1\u0026ndash;2,000 Hz). The signals were then digitized and stored on a personal computer via an A/D converter data acquisition system (ML880, ADInstruments, Dunedin, New Zealand) at a sampling frequency of 10 kHz.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Analysis\u003c/h2\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.5.1 Extracting parameters\u003c/h2\u003e\u003cp\u003eFollowing a previous study (Than et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), two amplitude-based parameters (\u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u0026minus;p\u003c/em\u003e\u003c/sub\u003e) and four time-based parameters (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003erise\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003ezero\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003efall\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003erec\u003c/em\u003e\u003c/sub\u003e) characterizing the MMG waveform were extracted (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). From the EMG recordings, the peak amplitude of the positive wave (\u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e) and the peak-to-peak amplitude (\u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u0026minus;p\u003c/em\u003e\u003c/sub\u003e) were extracted. Additionally, the time lag (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003edel\u003c/em\u003e\u003c/sub\u003e) between the first positive peaks of the MMG and EMG was calculated.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.5.2 Statistical analysis\u003c/h2\u003e\u003cp\u003eAs an index of absolute reliability, the percentage of the standard error of measurement to the grand mean (%SEM) was calculated using the following Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (de Vet et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Paravlić et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e):\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\text{%}\\text{S}\\text{E}\\text{M}=\\frac{\\sqrt{\\text{W}\\text{M}\\text{S}}}{\\text{G}\\text{r}\\text{a}\\text{n}\\text{d}\\:\\text{m}\\text{e}\\text{a}\\text{n}}\\times\\:100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e,\u003c/p\u003e\u003cp\u003ewhere WMS denotes the within-participant mean squares from one-way analysis of variance. Another index, the coefficient of variation (CV), was calculated as the mean of individual coefficients of variation, defined as the standard deviation of repeated measurements for each participant divided by the mean and multiplied by 100 (Atkinson et al. 1998).\u003c/p\u003e\u003cp\u003eTo evaluate the relative reliability of the observed data, the intraclass correlation coefficient based on a one-way random effects model, single-measurement type\u0026mdash;ICC(1,1), was calculated using the following Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) (Gwet \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Koo and Li \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e):\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\text{I}\\text{C}\\text{C}\\left(\\text{1,1}\\right)=\\frac{\\text{B}\\text{M}\\text{S}\\:-\\:\\text{W}\\text{M}\\text{S}}{\\text{B}\\text{M}\\text{S}\\:+\\:\\left(K\\:-\\:1\\right)\\:\\text{W}\\text{M}\\text{S}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e,\u003c/p\u003e\u003cp\u003ewhere BMS represents the between-participant mean squares from a one-way analysis of variance, and \u003cem\u003eK\u003c/em\u003e is the number of repeated measurements per participant. Since relative reliability can be improved by using the mean of repeated measurements, the intraclass correlation coefficient based on the one-way random effects model, mean-measurement type\u0026mdash;ICC(1,\u003cem\u003ek\u003c/em\u003e)\u0026mdash;was also calculated using the following Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e):\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:\\text{I}\\text{C}\\text{C}\\left(1,\\text{k}\\right)=\\frac{\\text{B}\\text{M}\\text{S}\\:-\\:\\text{W}\\text{M}\\text{S}}{\\text{B}\\text{M}\\text{S}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e.\u003c/p\u003e\u003cp\u003eHere, the mean data was computed from \u003cem\u003ek\u003c/em\u003e observations (\u003cem\u003ek\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2,3,..., \u003cem\u003eK\u003c/em\u003e), randomly selected from among the \u003cem\u003eK\u003c/em\u003e repeated measurements. These ICC estimates were reported with 95% confidence intervals (CIs), and their reliability levels were interpreted according to standard guidelines: values of \u0026lt;\u0026thinsp;0.5, 0.5\u0026ndash;0.75, 0.75\u0026ndash;0.9, and \u0026gt;\u0026thinsp;0.9 indicate poor, moderate, good, and excellent reliability, respectively (Koo and Li \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo evaluate correlations between any two parameters, both Spearman\u0026rsquo;s rank correlation coefficient (\u003cem\u003er\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e) and Pearson\u0026rsquo;s correlation coefficient (\u003cem\u003er\u003c/em\u003e) were calculated. All statistical analyses were conducted using STATA 11.1 (StataCorp LLC., Texas, USA).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.5.3 Spectral analysis\u003c/h2\u003e\u003cp\u003eThe power spectrum of the MMG signal was calculated using a fast Fourier transform (FFT) algorithm. The analysis window (data length\u0026thinsp;=\u0026thinsp;0.2048 s, 2048 points) was defined by extracting the segment from \u0026minus;\u0026thinsp;0.8 ms (i.e., 0.8 ms before the stimulus) to 50.4 ms of the MMG recording. A Tukey window (window width\u0026thinsp;=\u0026thinsp;0.0512 s, 512 points) was applied, and zero was assigned to the remaining data points. The mean power frequency (MPF) was then calculated from the 0\u0026ndash;200 Hz frequency band of the resulting spectrum.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the MMG waveform of the orbicularis oculi muscle evoked by supramaximal intensity stimulation consistently exhibited an initial positive wave with the highest peak amplitude, followed by a negative wave in all participants. However, the characteristics of the decaying oscillations following the negative wave varied among individuals.\u003c/p\u003e\u003cp\u003eThe results of the reliability indices for each parameter characterizing the MMG waveform are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In terms of absolute reliability, %SEM values were below 9% for all parameters except for \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003erec\u003c/em\u003e\u003c/sub\u003e, which exceeded 10% substantially. The CVs were \u0026le;\u0026thinsp;6.20% for all parameters except \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003erec\u003c/em\u003e\u003c/sub\u003e, where they also exceeded 10%. Regarding relative reliability, the 95% CIs of ICC(1,1) for the amplitude-derived parameters (\u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u0026minus;p\u003c/em\u003e\u003c/sub\u003e) clearly surpassed 0.90. For the time-derived parameters, the 95% CIs of ICC(1,1) ranged from 0.75 to 1.00 for \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003erise\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003ezero\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003efall\u003c/em\u003e\u003c/sub\u003e, whereas the CI for \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003erec\u003c/em\u003e\u003c/sub\u003e was below 0.75. However, the 95% CIs of ICC(1,\u003cem\u003ek\u003c/em\u003e) for \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003erec\u003c/em\u003e\u003c/sub\u003e increased with the number of averaged trials (\u003cem\u003ek\u003c/em\u003e), exceeding 0.75 when \u003cem\u003ek\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eRelative and absolute reliabilities of each parameter extracted from the evoked mechanomyogram (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;25)\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=\"char\" char=\"\u0026plusmn;\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter (Units)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrand mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eICC(1,1) (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%SEM (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCV (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e (m s\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e6.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.973 (0.955\u0026ndash;0.986)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u0026minus;p\u003c/em\u003e\u003c/sub\u003e (m s\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e10.18\u0026thinsp;\u0026plusmn;\u0026thinsp;2.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.973 (0.954\u0026ndash;0.986)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003erise\u003c/em\u003e\u003c/sub\u003e (ms)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e3.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.928 (0.883\u0026ndash;0.962)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003ezero\u003c/em\u003e\u003c/sub\u003e (ms)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e5.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.862 (0.784\u0026ndash;0.926)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003efall\u003c/em\u003e\u003c/sub\u003e (ms)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e9.87\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.904 (0.846\u0026ndash;0.949)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003erec\u003c/em\u003e\u003c/sub\u003e (ms)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e4.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.574 (0.428\u0026ndash;0.734)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eICC(1,1) (95% CI)- intraclass correlation coefficient estimate with 95% confidence interval based on single measurements; %SEM- standard error of measurement divided by the grand mean and multiplied by 100; CV- coefficient of variation\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eRepresentative EMG and MMG recordings of the orbicularis oculi muscle at various stimulus intensities are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed. Both EMG and MMG peak amplitudes increased with stimulus intensity, while waveform shapes remained relatively stable.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eChanges in the normalized peak amplitudes of EMG and MMG with varying stimulus levels are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (b, c, e, f). In the submaximal stimulus intensity range, there was a strong correlation between stimulus intensity and MMG amplitudes (\u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.851, \u003cem\u003er\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e = 0.860, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u0026minus;p\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.851, \u003cem\u003er\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e = 0.861, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Similarly, EMG amplitudes were highly correlated with stimulus level (\u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.900, \u003cem\u003er\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e = 0.905, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u0026minus;p\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.901, \u003cem\u003er\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e = 0.906, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Additionally, the normalized amplitudes of EMG and MMG showed very high correlations in this range (\u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e vs. \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.932, \u003cem\u003er\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e = 0.930, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u0026minus;p\u003c/em\u003e\u003c/sub\u003e vs. \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u0026minus;p\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.937, \u003cem\u003er\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e = 0.933, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(a), the total and peak power spectra estimated from the MMG recordings in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(d) were highest at the 100% stimulus level (thick line) and decreased progressively with lower stimulus levels. All spectra displayed a unimodal shape, with more than 80% of the total power distributed between 5 Hz and 70 Hz, and the upper limit of the frequency components reaching no more than 100 Hz. Additionally, spectral shape and bandwidth showed minimal change across stimulus intensities. These trends were consistent across all participants.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor all participants, the MPF showed no statistically significant correlation with stimulus intensity and remained approximately constant at around 33 Hz (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(b)). Normalized MPF also did not correlate significantly with stimulus level, although greater variability was observed below the 70% stimulus threshold (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(c)).\u003c/p\u003e\u003cp\u003eThe first positive peak of the MMG waveform consistently appeared later than that of the EMG in all participants. The time delay of MMG relative to EMG (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003edel\u003c/em\u003e\u003c/sub\u003e) remained nearly constant across stimulus levels (5.52\u0026thinsp;\u0026plusmn;\u0026thinsp;2.10 ms) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e(a)). Similarly, normalized \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003edel\u003c/em\u003e\u003c/sub\u003e showed no significant correlation with stimulus level, though its variability increased at lower stimulus intensities (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e(b)).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe evoked MMGs of the orbicularis oculi muscle exhibited waveforms resembling underdamped oscillations, beginning with a positive wave of maximum amplitude (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This pattern is consistent with previously reported MMG waveforms of limb muscles during twitch contractions (Bichler \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Orizio et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). According to Bichler (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), the initial and largest amplitude fluctuations in MMG recordings occur during the contraction phase of a twitch and are associated with force generation in the muscle fibers. The report also noted that the slower oscillations observed during the subsequent relaxation phase\u0026mdash;when twitch tension decreases\u0026mdash;were attributed to the characteristics of the transducer (a piezoelectric microphone) used in those measurements. However, that study employed paraffin oil to fill the space between the microphone and the muscle surface, which differs from our method, where accelerometers were directly attached to the skin over the muscle. Because accelerometers detect object acceleration, our recordings likely reflect the fluctuating acceleration of the skin surface. It is generally understood that the twitch tension behavior during the relaxation phase is governed by the passive mechanical properties of muscle. In mathematical models of skeletal muscle, tension decay during relaxation is represented by a viscoelastic element composed of muscle fibers and surrounding connective tissue (Lemos et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Phillips et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Given the relationship between geometric changes in muscle fibers and vibrations on the skin surface, the oscillations observed after the negative wave in accelerometer-recorded signals are likely to reflect these passive viscoelastic properties of both the muscle and the skin\u0026mdash;rather than being artifacts of the transducer itself.\u003c/p\u003e\u003cp\u003eThis study examined the reliability of six parameters characterizing the initial oscillations of the evoked MMG of the orbicularis oculi muscle under supramaximal stimulation. According to previous reports (C\u0026egrave; et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ng et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), acceptable thresholds for reliability are %SEM and CV values below 10%. Based on their criteria, the %SEM and CV results suggest that all parameters\u0026mdash;except \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003erec\u003c/em\u003e\u003c/sub\u003e\u0026mdash;demonstrate sufficient absolute reliability. The ICC(1,1) values indicate that all parameters except \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003erec\u003c/em\u003e\u003c/sub\u003e were rated as excellent or good to excellent according to established guidelines, reflecting high relative reliability. The lower reliability of \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003erec\u003c/em\u003e\u003c/sub\u003e may be attributed to the fact that, among the six parameters, only \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003erec\u003c/em\u003e\u003c/sub\u003e is associated with the relaxation phase of the twitch contraction. However, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003erec\u003c/em\u003e\u003c/sub\u003e also achieves sufficiently high relative reliability (Good to Excellent) when averaged over five or more repeated measurements.\u003c/p\u003e\u003cp\u003eEMG amplitude has frequently been used to assess the intensity of the orbicularis oculi muscle response (Unal et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Valls-Sol\u0026eacute; et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). The amplitude of evoked EMG is generally known to increase proportionally with stimulus intensity, and our results confirmed this linear relationship in the orbicularis oculi muscle up to the 100% stimulus level (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(b, c)). Conversely, based on known MMG characteristics, the rate of increase in evoked MMG amplitude is expected to change if the type of recruited muscle fiber shifts with increasing stimulus intensity. Specifically, MMG amplitude generated by slow-twitch (ST) fibers is lower than that from fast-twitch (FT) fibers, meaning that the amplitude increase per additional active fiber is also small (Marchetti et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). During twitch contractions elicited by motor nerve stimulation, FT fibers are recruited first\u0026mdash;contrary to the \u0026ldquo;size principle\u0026rdquo;\u0026mdash;followed by ST fibers as stimulus intensity rises (Baratta et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). Consequently, the rate of MMG amplitude increase might be expected to slow beyond a certain stimulus level. However, the present results show that, similar to EMG, MMG amplitude also increases linearly with stimulus intensity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(e, f)). In addition, a strong correlation between EMG and MMG amplitudes was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These findings suggest that the peak amplitude of MMG, like EMG, can serve as a reliable indicator of the response intensity of the orbicularis oculi muscle. The linear increase in MMG amplitude may be attributed to the muscle\u0026rsquo;s structural characteristics. According to previous anatomical studies (Hwang et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Johnson et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1973\u003c/span\u003e), FT fibers constitute more than 80\u0026ndash;90% of the orbicularis oculi muscle, which could account for the observed linearity in MMG response. MMG is known to reflect the activity of muscle fibers located closer to the surface more strongly than that of deeper fibers (Orizio \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). As a result, the activity of ST fibers\u0026mdash;which are fewer in number and typically located deeper within the muscle\u0026mdash;may have little to no influence on the evoked MMG of the orbicularis oculi. This interpretation is supported by our spectral analysis results (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Specifically, since MMG signals from FT fibers are known to contain higher frequency components than those from ST fibers (Orizio \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), any change in the type of active muscle fibers should be reflected in the spectral shape, bandwidth, or mean frequency. However, our findings showed no changes in these spectral characteristics across different stimulus levels, suggesting no apparent influence of ST fiber recruitment on the MMG.\u003c/p\u003e\u003cp\u003eOur results showed that the first positive peak of the MMG occurred clearly later than that of the EMG (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The delay time (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003edel\u003c/em\u003e\u003c/sub\u003e) remained nearly constant at approximately 5.5 ms, regardless of stimulus level. According to a previous study (Sun et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), there is a delay of about 10 ms between the action potential and the onset of force development due to the excitation-contraction coupling process. MMGs recorded with laser displacement meters\u0026mdash;which reflect skin displacement\u0026mdash;have been reported to precede twitch tension due to differences in the propagation pathway (Orizio et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). From a mathematical perspective, skin acceleration is expected to reach its maximum shortly after the onset of skin displacement. Therefore, the consistent time lag (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003edel\u003c/em\u003e\u003c/sub\u003e) between MMG and EMG, independent of stimulus intensity and averaging about 5.5 ms, may be directly related to the muscle\u0026rsquo;s force generation process.\u003c/p\u003e\u003cp\u003eTraditionally, the time from stimulus to the peak EMG response is used as a latency marker in reflex pathway assessments (Unal et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Valls-Sol\u0026eacute; et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Thus, our findings suggest that the time from stimulus to the peak MMG response can also serve as a suitable indicator for reflex pathway evaluation, despite differences in absolute values. Moreover, analysis of \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003edel\u003c/em\u003e\u003c/sub\u003e may provide additional insights into changes in the muscle force generation process.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe mechanomyographic twitch response of the orbicularis oculi muscle, as detected by accelerometry, demonstrated sufficiently high absolute and relative reliability. Furthermore, the amplitude and latency of the MMG\u0026rsquo;s first positive peak\u0026mdash;similar to conventional EMG\u0026mdash;were shown to be valid indicators for assessing the intensity of the orbicularis oculi muscle response and the integrity of reflex pathways.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eEthics Approval\u003c/h2\u003e\n\u003cp\u003eThe study complies with the principles laid down in the Declaration of Helsinki. The Ethics Committee of Osaka Electro-Communication University approved the experimental protocol (Approval number: 22\u0026thinsp;\u0026minus;\u0026thinsp;013, Approval date: 1 March 2023).\u003c/p\u003e\n\u003ch2\u003eConsent to Participate\u003c/h2\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research was supported by Grants-in-Aid for Scientific Research (Grant number 23K07026 to K.I.).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eConceptualization: Y.I. and K.A.; Methodology: Y.I., K.A., and K.M.; Formal analysis and investigation: Y.I. and C.M.; Writing \u0026ndash; original draft preparation: Y.I.; Writing\u0026ndash; review \u0026amp; editing: K.A. and K.I.; Funding acquisition: K.I.; Resources: K.A. and K.I.; Supervision: K.M.; Final version of the manuscript: all authors.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe authors would like to thank all the participants involved in the study for their patience and committed involvement. The authors would also like to thank Enago (www.enago.jp) for the English language review. We gratefully acknowledge the support provided by Osaka Electro-Communication University for this study.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAkataki K, Mita K, Itoh Y (1999) Relationship between mechanomyogram and force during voluntary contractions reinvestigated using spectral decomposition. 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Med Biol Eng Comput 41:198\u0026ndash;202. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF02344888\u003c/span\u003e\u003cspan address=\"10.1007/BF02344888\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"accelerometer, mechanomyogram, orbicularis oculi muscle, reliability, stimulus response","lastPublishedDoi":"10.21203/rs.3.rs-8130145/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8130145/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction/Aims\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eElectromyograms (EMGs) of the orbicularis oculi muscle are commonly used to assess the blink reflex, which provides clinically important information. However, mechanomyograms (MMGs) may serve as a promising alternative. This study aimed to evaluate the utility of MMGs in assessing orbicularis oculi responses to stimulation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eElectrical stimulation was applied to the facial nerve axons of 25 healthy adult participants, and MMGs were recorded using an accelerometer. The reliability of amplitude-based parameters (\u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003ep−p\u003c/em\u003e\u003c/sub\u003e) and time-based parameters (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003erise\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003ezero\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003efall\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003erec\u003c/em\u003e\u003c/sub\u003e) characterizing MMG waveforms was assessed using the intraclass correlation coefficient (ICC), standard error of measurement (SEM), and coefficient of variation (CV). Additionally, the amplitude and temporal characteristics of MMGs evoked by varying stimulus intensities were compared with simultaneously recorded EMGs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll parameters, except \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003erec\u003c/em\u003e\u003c/sub\u003e, showed high relative reliability (95% confidence interval of ICC of \u0026gt; 0.75) and high absolute reliability (%SEM and CV \u0026lt; 10%). MMG amplitudes (\u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003ep−p\u003c/em\u003e\u003c/sub\u003e) and EMG amplitudes (\u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003ep−p\u003c/em\u003e\u003c/sub\u003e) increased linearly with stimulus intensity. A strong correlation was found between the amplitudes of both signals (\u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e vs. \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e: \u003cem\u003er\u003c/em\u003e = 0.932, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001; \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003ep−p\u003c/em\u003e\u003c/sub\u003e vs. \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003ep−p\u003c/em\u003e\u003c/sub\u003e: \u003cem\u003er\u003c/em\u003e = 0.937, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001). The first positive MMG peak consistently appeared at a fixed interval (5.52 ± 2.10 ms) after the EMG peak, irrespective of stimulus level.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese findings suggest that MMG amplitude and latency (stimulus-to-peak time) are reliable indicators of orbicularis oculi muscle activity and can be effectively used alongside EMG in clinical and research settings.\u003c/p\u003e","manuscriptTitle":"Accelerometer-based characteristics of evoked mechanomyograms in the orbicularis oculi muscle","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-28 06:58:48","doi":"10.21203/rs.3.rs-8130145/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4d13efc8-45ab-4069-9e7e-dc3e7ff64fb7","owner":[],"postedDate":"November 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:06:17+00:00","versionOfRecord":{"articleIdentity":"rs-8130145","link":"https://doi.org/10.1007/s00221-026-07249-2","journal":{"identity":"experimental-brain-research","isVorOnly":false,"title":"Experimental Brain Research"},"publishedOn":"2026-02-21 15:59:31","publishedOnDateReadable":"February 21st, 2026"},"versionCreatedAt":"2025-11-28 06:58:48","video":"","vorDoi":"10.1007/s00221-026-07249-2","vorDoiUrl":"https://doi.org/10.1007/s00221-026-07249-2","workflowStages":[]},"version":"v1","identity":"rs-8130145","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8130145","identity":"rs-8130145","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}