Electromyographic Patterns Research Articles

Influence of Rigid and Flexible Occlusal Stents on Electromyographic Patterns in Individuals Exhibiting Different Degrees of Occlusal Wear

Influence of Rigid and Flexible Occlusal Stents on Electromyographic Patterns in Individuals Exhibiting Different Degrees of Occlusal Wear

Electromyographical Analysis of The Thigh Muscle During Four Yogasana

The goal of the study was to examine the peak EMG at four lower body muscles during four different yoga poses performed by yoga practitioners. Materials and methods: Male Yoga practitioners (n= 11) were assessed for peak electromyography (EMG) of VM-Vastus Medialis, VL- Vastus lateralis, BF- Biceps femoris, ST- Semitendinosus, while performing four Yogasana: UK- Utkatasana, VB- Virabhadrasana II, UPE- Urdhva prasarita ekapadasana six, DOL- Dolasana (pendulum pose). Results: The UK induced the highest EMG for VM (124.5000 ± 25.36088), VL(124.2364 ± 27.35410), ST (106.6091 ± 32.77619),BF (96.6909 ± 29.34824),The VB II induced the highest EMG for the VL (102.0727 ± 32.96262), VM (100.4091± 16.30960), BF (84.5727 ± 28.63844), ST (86.2818 ± 26.07239).The UPE-Six induced the highest EMG for the VM (42.4364 ± 17.09025), BF (45.0636 ± 21.33440),ST (39.3727 ± 17.03315),VL (38.3636 ± 20.73612).The DOL induced the highest EMG for the VM (88.7455 ± 56.13069), BF (65.2545 ± 36.53172), ST (62.6455 ± 19.28302),VL (43.2545 ± 24.14255). However, between Yogasana, only the VM showed a significant (P < .000) * different EMG. Specifically, the UK induced greater VM EMG compared to UPE (82.064, P < .000), VB (57.973, P < .000) * Conclusions: Peak muscular activation in the four lowerbody muscles can change depending on how the yoga players treat the four differences. A declining pattern of peak EMG for the UK > VB II > DOL and UPE six Yogasana, however, indicates that variations between poses appear to be minimal, with the exception of the VM muscle.

Domestic cat larynges can produce purring frequencies without neural input

Most mammals produce vocal sounds according to the myoelastic-aerodynamic (MEAD) principle, through self-sustaining oscillation of laryngeal tissues.1,2 In contrast, cats have long been believed to produce their low-frequency purr vocalizations through a radically different mechanism involving active muscle contractions (AMC), where neurally driven electromyographic burst patterns (typically at 20-30Hz) cause the intrinsic laryngeal muscles to actively modulate the respiratory airflow. Direct empirical evidence for this AMC mechanism is sparse.3 Here, the fundamental frequency (fo) ranges of eight domestic cats (Felis silvestris catus) were investigated in an excised larynx setup, to test the prediction of the AMC hypothesis that vibration should be impossible without neuromuscular activity, and thus unattainable in excised larynx setups, which are based on MEAD principles. Surprisingly, all eight excised larynges produced self-sustained oscillations at typical cat purring rates. Histological analysis of cat larynges revealed the presence of connective tissue masses, up to 4mm in diameter, embedded in the vocal fold.4 This vocal fold specialization appears to allow the unusually low fo values observed in purring. While our data do not fully reject the AMC hypothesis for purring, they show that cat larynges can easily produce sounds in the purr regime with fundamental frequencies of 25 to 30Hz without neural input or muscular contraction. This strongly suggests that the physical and physiological basis of cat purring involves the same MEAD-based mechanisms as other cat vocalizations (e.g., meows) and most other vertebrate vocalizations but is potentially augmented by AMC.

Identifying Referent Control Variables Underlying Goal-Directed Arm Movements.

The referent control theory (RCT) for action and perception is an advanced formulation of the equilibrium-point hypothesis. The RCT suggests that rather than directly specifying the desired motor outcome, the nervous system controls action and perception indirectly by setting the values of parameters of physical and physiological laws. This is done independently of values of kinematic and kinetic variables including electromyographic patterns describing the motor outcome. One such parameter-the threshold muscle length, λ, at which motoneurons of a given muscle begin to be recruited, has been identified experimentally. In RCT, a similar parameter, the referent arm position, R, has been defined for multiple arm muscles as the threshold arm position at which arm muscles can be quiescent but activated depending on the deflection of the actual arm position, Q, from R. Changes in R result in reciprocal changes in the activity of opposing muscle groups. We advanced the explanatory power of RCT by combining the usual biomechanical descriptions of motor actions with the identification of the timing of R underlying arm movements made with reversals in three directions and to three different extents. We found that in all movements, periods of minimization of the activity of multiple muscles could be identified at ∼61%-86% of the reaching extent in each direction. These electromyographic minimization periods reflect the spatial coordinates at which the R and Q overlap during the production of movements with reversals. The findings support the concept of the production of arm movement by shifting R.

Phase shift between joint rotation and actuation reflects dominant forces and predicts muscle activation patterns.

During behavior, the work done by actuators on the body can be resisted by the body's inertia, elastic forces, gravity, or viscosity. The dominant forces that resist actuation have major consequences on the control of that behavior. In the literature, features and actuation of locomotion, for example, have been successfully predicted by nondimensional numbers (e.g. Froude number and Reynolds number) that generally express the ratio between two of these forces (gravitational, inertial, elastic, and viscous). However, animals of different sizes or motions at different speeds may not share the same dominant forces within a behavior, making ratios of just two of these forces less useful. Thus, for a broad comparison of behavior across many orders of magnitude of limb length and cycle period, a dimensionless number that includes gravitational, inertial, elastic, and viscous forces is needed. This study proposes a nondimensional number that relates these four forces: the phase shift (ϕ) between the displacement of the limb and the actuator force that moves it. Using allometric scaling laws, ϕ for terrestrial walking is expressed as a function of the limb length and the cycle period at which the limb steps. Scale-dependent values of ϕ are used to explain and predict the electromyographic (EMG) patterns employed by different animals as they walk.

Estimating finger joint angles by surface EMG signal using feature extraction and transformer-based deep learning model

Human-machine interfaces frequently use electromyography (EMG) signals. Based on previous work, feature extraction has a great deal of influence on the performance of EMG pattern recognition. Furthermore, the Deep Learning method is supposed to increase performance and not depend on feature engineering. However, directly processing raw signals will require a higher computation rate. This study proposed a new method that combines feature extraction and Deep Learning to address those issues while improving performance, reducing architecture size, and producing a more representative output. The proposed architecture employs the Transformer model as the backbone to get the correlation between elements and focus on the important information for estimating the flexion-extension of finger joint angles. This study uses experiment three of the NinaPro (Non-Invasive Adaptive Hand Prosthetics) DB5 dataset. Each experiment produces 16 Surface EMG data streams from two Myo Armbands devices representing 22 finger joint angles as output. This study compares the windowing process, feature extraction, execution time, and results with previous studies. The results show that the proposed model outperforms the previous study, from 0.957 in the previous study to become 0.970 in this study for the R-Square score. This result is obtained using 100 data points for the windowing process and Median Frequency for the best feature extraction method.

Consistency of muscle activation signatures across different walking speeds

BackgroundUsing a machine learning algorithm, individuals can be accurately identified from their muscle activation patterns during gait, leading to the concept of individual muscle activation signatures. Research questionAre muscle activation signatures robust across different walking speeds? MethodsWe used an open dataset containing electromyographic (EMG) signals from 8 lower limb muscles in 50 asymptomatic adults walking at 5 speeds (extremely slow, very slow, slow, spontaneous, and fast). A machine learning approach classified the EMG profiles based on similar (intra-speed classification) or different (inter-speed classification) walking speeds as training and testing conditions. ResultsIntra-speed median classification rates of muscle activation profiles increased with walking speed, from 92 % for extremely slow, to 100 % for self-selected fast walking conditions. Inter-speed median classification rates increased when the speed of the training condition was closer to that of the testing condition. Higher median classification rates were found across slow, spontaneous, and fast walking speed conditions, from 56 % to 96 %, compared with classification rates involving extremely and very slow walking speed conditions, from 6 % to 62 %. SignificanceOur findings reveal that i) muscle activation signatures are detectable for a large range of walking speeds, even those involving different gait strategies (intra-speed median classification rates from 92 % to 100 %), and ii) muscle activation signatures observed during very low walking speeds are not consistent with those observed at higher speeds, suggesting a difference in motor control strategy. Caution should therefore be exercised when assessing gait deviations of a slow walking patient against a normative database obtained at higher speed. Identifying the robustness of individual muscle activation signatures across different movements could help in detecting changes in motor control, otherwise difficult to detect on classical time-varying EMG patterns.

A Novel Event-Driven Spiking Convolutional Neural Network for Electromyography Pattern Recognition.

Electromyography (EMG) pattern recognition is an important technology for prosthesis control and human-computer interaction etc. However, the practical application of EMG pattern recognition is hampered by poor accuracy and robustness due to electrode shift caused by repeated wearing of the signal acquisition device. Moreover, the user's acceptability is low due to the heavy training burden, which is caused by the need for a large amount of training data by traditional methods. In order to explore the advantage of spiking neural network (SNN) in solving the poor robustness and heavy training burden problems in EMG pattern recognition, a spiking convolutional neural network (SCNN) composed of cyclic convolutional neural network (CNN) and fully connected modules is proposed and implemented in this study. High density surface electromyography (HD-sEMG) signals collected from 6 gestures of 10 subjects at 6 electrode positions are taken as the research object. Compared to CNN with the same structure, CNN-Long Short Term Memory (CNN-LSTM), linear kernel linear discriminant analysis classifier (LDA) and spiking multilayer perceptron (Spiking MLP), the accuracy of SCNN is 50.69%, 33.92%, 32.94% and 9.41% higher in the small sample training experiment, 6.50%, 4.23%, 28.73%, and 2.57% higher in the electrode shifts experiment respectively. In addition, the power consumption of SCNN is about 1/93 of CNN. The advantages of the proposed framework in alleviating user training burden, mitigating the adverse effect of electrode shifts and reducing power consumption make it very meaningful for promoting the development of user-friendly real-time myoelectric control system.

SEMG-Based Gesture Recognition Using Temporal History.

Surface electromyography (sEMG) patterns have been decoded using learning-based methods that determine complicated nonlinear decision boundaries. However, overlapping classes in sEMG pattern recognition still degrade the classification accuracy because they cannot be separated by the decision boundaries. We hypothesized that certain overlapping classes can be separated while tracing the temporal history of sEMG patterns. Therefore, a novel post-processing method is proposed to adjust classification errors using the separated patterns from the temporal history of overlapping classes. The proposed method confirms the confidence of the prediction result by calculating the instantaneous pattern separability for the sequential sEMG input. The prediction result with high separability pattern is considered to have a high confidence of being correct (reliable). This result is stored for adjusting the next sEMG input. When the subsequent prediction is identified as having low confidence (unreliable), the predicted result is adjusted using the stored reliable predicted results. The proposed method adds an adjustment step to an existing classifier (maximum likelihood classifier (MLC), k-nearest neighbor (KNN), and support vector machine (SVM)), such that it can be attached to the back-end regardless of the type of classifier. Ten subjects performed 13 types of hand gestures, including overlapping patterns. The overall classification accuracy was enhanced to 88.93%(+8.12%p, MLC), 91.31%(+7.68%p, KNN), and 99.65%(+11.63%p, SVM) after the implementation of the proposed post-processing. Additionally, a faster and more accurate gesture classification was achieved with accuracy enhancement before gesture completion as 85.62%(+4.23%p, MLC), 89.77%(+4.23%p, KNN), and 97.62%(+11.12%p, SVM).

Development of a New Wearable Device for the Characterization of Hand Tremor.

Rest tremor (RT) is observed in subjects with Parkinson's disease (PD) and Essential Tremor (ET). Electromyography (EMG) studies have shown that PD subjects exhibit alternating contractions of antagonistic muscles involved in tremors, while the contraction pattern of antagonistic muscles is synchronous in ET subjects. Therefore, the RT pattern can be used as a potential biomarker for differentiating PD from ET subjects. In this study, we developed a new wearable device and method for differentiating alternating from a synchronous RT pattern using inertial data. The novelty of our approach relies on the fact that the evaluation of synchronous or alternating tremor patterns using inertial sensors has never been described so far, and current approaches to evaluate the tremor patterns are based on surface EMG, which may be difficult to carry out for non-specialized operators. This new device, named "RT-Ring", is based on a six-axis inertial measurement unit and a Bluetooth Low-Energy microprocessor, and can be worn on a finger of the tremulous hand. A mobile app guides the operator through the whole acquisition process of inertial data from the hand with RT, and the prediction of tremor patterns is performed on a remote server through machine learning (ML) models. We used two decision tree-based algorithms, XGBoost and Random Forest, which were trained on features extracted from inertial data and achieved a classification accuracy of 92% and 89%, respectively, in differentiating alternating from synchronous tremor segments in the validation set. Finally, the classification response (alternating or synchronous RT pattern) is shown to the operator on the mobile app within a few seconds. This study is the first to demonstrate that different electromyographic tremor patterns have their counterparts in terms of rhythmic movement features, thus making inertial data suitable for predicting the muscular contraction pattern of tremors.

Investigation with able-bodied subjects suggests Myosuit may potentially serve as a stair ascent training robot

Real world settings are seldomly just composed of level surfaces and stairs are frequently encountered in daily life. Unfortunately, ~ 90% of the elderly population use some sort of compensation pattern in order to negotiate stairs. Because the biomechanics required to successfully ascend stairs is significantly different from level walking, an independent training protocol is warranted. Here, we present as a preliminary investigation with 11 able-bodied subjects, prior to clinical trials, whether Myosuit could potentially serve as a stair ascent training robot. Myosuit is a soft wearable exosuit that was designed to assist the user via hip and knee extension during the early stance phase. We hypothesized that clinical studies could be carried out if the lower limb kinematics, sensory feedback via plantar force, and electromyography (EMG) patterns do not deviate from the user’s physiological stair ascent patterns while reducing hip and knee extensor demand. Our results suggest that Myosuit conserves the user’s physiological kinematic and plantar force patterns. Moreover, we observe approximately 20% and 30% decrease in gluteus maximus and vastus medialis EMG levels in the pull up phase, respectively. Collectively, Myosuit reduces the hip and knee extensor demand during stair ascent without any introduction of significant compensation patterns.

Effects of tendon elastic energy and electromyographic activity pattern on jumping height and pre-stretch augmentation during jumps with different pre-stretch intensity

ABSTRACT The present study aimed to investigate the effects of tendon elastic energy and electromyographic activity patterns (ratio of pre-landing to concentric: mEMG PLA/CON; ratio of eccentric to concentric; mEMG ECC/CON) on jump performance. Twenty-nine males performed five kinds of unilateral jumps using only ankle joint (no-countermovement jump: noCMJ; countermovement jump: CMJ; drop jumps at 10, 20 and 30 cm drop height: DJ10, DJ20 and DJ30). Jumping height, pre-stretch augmentation and electromyographic activity of the plantar flexor muscles were measured. The elastic energy of the Achilles tendon was measured during isometric contractions. Relative tendon elastic energy (to body mass) was highly correlated with jumping heights of CMJ, DJ10 and DJ20 but not with noCMJ and DJ30, whereas that was significantly correlated with pre-stretch augmentation in CMJ, but not with three DJs. The mEMG PLA/CON was significantly correlated with the pre-stretch augmentation of DJ20 and DJ30, but not with DJ10, whereas the mEMG ECC/CON was significantly correlated with the pre-stretch augmentation of DJ20 and DJ30, but not with CMJ and DJ10. These results suggested that jumping exercises with low pre-stretch intensity benefited from tendon elastic energy, but those with high pre-stretch intensity benefited from electromyographic activity patterns.

Feasibility study on the application of a spiking neural network in myoelectric control systems.

In recent years, the effectiveness of a spiking neural network (SNN) for Electromyography (EMG) pattern recognition has been validated, but there is a lack of comprehensive consideration of the problems of heavy training burden, poor robustness, and high energy consumption in the application of actual myoelectric control systems. In order to explore the feasibility of the application of SNN in actual myoelectric control systems, this paper investigated an EMG pattern recognition scheme based on SNN. To alleviate the differences in EMG distribution caused by electrode shifts and individual differences, the adaptive threshold encoding was applied to gesture sample encoding. To improve the feature extraction ability of SNN, the leaky-integrate-and-fire (LIF) neuron that combines voltage-current effect was adopted as a spike neuron model. To balance recognition accuracy and power consumption, experiments were designed to determine encoding parameter and LIF neuron release threshold. By conducting the gesture recognition experiments considering different training test ratios, electrode shifts, and user independences on the nine-gesture high-density and low-density EMG datasets respectively, the advantages of the proposed SNN-based scheme have been verified. Compared with a Convolutional Neural Network (CNN), Long Short-Term Memory Network (LSTM) and Linear Discriminant Analysis (LDA), SNN can effectively reduce the number of repetitions in the training set, and its power consumption was reduced by 1-2 orders of magnitude. For the high-density and low-density EMG datasets, SNN improved the overall average accuracies by about (0.99 ~ 14.91%) under different training test ratios. For the high-density EMG dataset, the accuracy of SNN was improved by (0.94 ~ 13.76%) under electrode-shift condition and (3.81 ~ 18.95%) in user-independent case. The advantages of SNN in alleviating the user training burden, reducing power consumption, and improving robustness are of great significance for the implementation of user-friendly low-power myoelectric control systems.

Surface electromyographic wheel speed skate protocol and its potential in athletes' performance analysis and injury prevention.

Speed skating is a discipline that involves cyclical and repetitive movements that make athletes susceptible to injuries, especially in the groin. In professional athletes, during a season, it was found that about 20% had overuse injuries with significant consequences during the competitive season due to long recovery times. Currently, new technological tools allow the measurement of multiple parameters and, through a panel of data deriving from various measurements, provide a valuable aid for training and rehabilitative purposes. This study aimed to probe the potential of the new analysis algorithm, or its ability to detect differences in electromyographic and acceleration patterns between newcomers to the discipline and professional athletes. We proceeded through measurements using a system based on an inertial sensor and four probes for surface electromyography. The analysis highlights important differences both from the point of view of accelerations (with marked oscillations on the three axes of the neophyte concerning greater stability of the professional's trunk) and a different pattern of muscle activation during joint movement a greater coactivation of the neophyte compared to the professional, which could lead to a greater risk of injury due to less training. This new protocol, when validated on a statistically significant sample of elite athletes leading to specific benchmarks, can be used to improve athletes' performances and maybe to prevent athletes' injuries.

Determinants of joint stiffness and jumping height during drop jump.

The present study aimed to examine the effects of muscle-tendon mechanical properties and electromyographic activity on joint stiffness and jumping height and to explore the determinants of joint stiffness and jumping height. Twenty-nine males performed unilateral drop jumps at three drop heights (10, 20, and 30 cm) using only the ankle joint on the sledge apparatus. Ankle joint stiffness, jumping height, and electromyographic activity of the plantar flexor muscles were measured during drop jumps. Active muscle stiffness of the medial gastrocnemius muscle was calculated according to changes in the estimated muscle force and fascicle length during fast stretching at five different angular velocities (100, 200, 300, 500, and 600 deg s-1 ) after submaximal isometric contractions. Tendon stiffness and elastic energy were measured during ramp and ballistic contractions. Active muscle stiffness was significantly correlated with joint stiffness, except for a few conditions. Tendon stiffness measured during ramp and ballistic contractions was not significantly correlated with joint stiffness. The ratios of electromyographic activity before landing and during the eccentric phase to that during the concentric phase were significantly correlated with joint stiffness. In addition, jumping heights at 10 and 20 cm (except for 30 cm) drop heights were strongly associated with the tendon elastic energy, whereas no other measured variables showed significant correlations with jumping heights. These results suggested that (1) joint stiffness is determined by active muscle stiffness and electromyographic activity patterns during jumping, and (2) jumping height is determined by tendon elastic energy.

Alternative muscle synergy patterns of upper limb amputees.

Myoelectric hand prostheses are effective tools for upper limb amputees to regain hand functions. Much progress has been made with pattern recognition algorithms to recognize surface electromyography (sEMG) patterns, but few attentions was placed on the amputees' motor learning process. Many potential myoelectric prostheses users could not fully master the control or had declined performance over time. It is possible that learning to produce distinct and consistent muscle activation patterns with the residual limb could help amputees better control the myoelectric prosthesis. In this study, we observed longitudinal effect of motor skill learning with 2 amputees who have developed alternative muscle activation patterns in response to the same set of target prosthetic actions. During a 10-week program, amputee participants were trained to produce distinct and constant muscle activations with visual feedback of live sEMG and without interaction with prosthesis. At the end, their sEMG patterns were different from each other and from non-amputee control groups. For certain intended hand motion, gradually reducing root mean square (RMS) variance was observed. The learning effect was also assessed with a CNN-LSTM mixture classifier designed for mobile sEMG pattern recognition. The classification accuracy had a rising trend over time, implicating potential performance improvement of myoelectric prosthesis control. A follow-up session took place 6 months after the program and showed lasting effect of the motor skill learning in terms of sEMG pattern classification accuracy. The results indicated that with proper feedback training, amputees could learn unique muscle activation patterns that allow them to trigger intended prosthesis functions, and the original motor control scheme is updated. The effect of such motor skill learning could help to improve myoelectric prosthetic control performance.

Influence of Soft Stabilization Splint on Electromyographic Patterns in Masticatory and Neck Muscles in Healthy Women.

This study investigates the influence of soft stabilization splints on electromyographic patterns in masticatory and neck muscles in healthy women. A total of 70 healthy women were qualified for the research. The resting and clenching electromyographic patterns of the temporalis (TA), masseter (MM), digastric (DA), and sternocleidomastoid (SCM) muscles were measured using the BioEMG III™ apparatus. The interaction between splint application and resting muscle activity affected the results in all examined muscles except the temporalis muscle. A large effect size was observed in masseter (2.19 µV vs. 5.18 µV; p = 0.00; ES = 1.00) and digastric (1.89 µV vs. 3.17 µV; p = 0.00; ES = 1.00) both-sided RMS activity. Significant differences between the two conditions were observed in all Functional Clenching Indices (FCI) for MM, SDM, and DA muscles. All FCI values for the MM and DA muscles were significantly lower with than without the splint. We observed an increase in all activity indices due to splint application, which suggests a masseter muscle advantage during measurement. The soft stabilization splint influenced resting and functional activity in the MM, SDM, and DA muscles. During tooth clenching, a soft stabilization splint changed the involvement proportions of the temporalis and masseter muscles, transferring the main activity to the masseter muscles. Using a soft stabilization splint did not affect the symmetry of the electromyographic activity of the masticatory and neck muscles.

Non-Functional Jaw Muscular Activity in Patients with Disorders of Consciousness Revealed by A Long-Lasting Polygraphy.

The presence of involuntary, non-functional jaw muscle activity (NFJMA) has not yet been assessed in patients with disorders of consciousness (DOC), although the presence of bruxism and other forms of movement disorders involving facial muscles is probably more frequent than believed. In this work, we evaluated twenty-two prolonged or chronic DOC patients with a long-lasting polygraphic recording to verify NFJMA occurrence and assess its neurophysiological patterns in this group of patients. A total of 5 out of 22 patients showed the presence of significant NFJMA with electromyographic patterns similar to what can be observed in non-DOC patients with bruxism, thus suggesting a disinhibition of masticatory motor nuclei from the cortical control. On the other hand, in two DOC patients, electromyographic patterns advised for the presence of myorhythmia, thus suggesting a brainstem/diencephalic involvement. Functional, non-invasive tools such as long-lasting polygraphic recordings should be extended to a larger sample of patients, since they are increasingly important in revealing disorders potentially severe and impacting the quality of life of DOC patients.

Classification of muscle activity patterns in healthy children using biclustering algorithm

In recent years, there has been major interest in recognising electromyography (EMG) patterns. This work proposes a new method based on a biclustering algorithm which can group strides showing homogeneous EMG activation intervals. The surface EMG signals of biceps femoris, rectus femoris, semitendinosus, lateral gastrocnemius, and medial gastrocnemius muscles of 17 healthy children aged between 4 and 11 years old were obtained using a Trigno EMG wireless system. The data set was tested for different values of parameter α (the threshold describing when the multiple node deletion step is used) and δ (the threshold that limits the value of the mean square residue). The highest number of coincidences of muscle activation was observed in 6 to 7-year-old subjects. This was not affected by their anthropometrics or gender. The obtained biclusters reflect actual differences between the subjects' gait parameters, namely stride length, stride time, and walking speed. These results can be used to develop strategies for finding homogeneous groups of patients.

The Influence of Visual Input on Electromyographic Activity and Patterns of Masticatory and Cervical Spine Muscles in Emmetropic Caucasian Subjects by Gender

(1) Background: The objective of the research was to analyze the change of visual input on electromyographic activity and patterns of masticatory and cervical spine muscles in emmetropic Caucasian subjects by gender. Supposedly, visual input should not influence activity and electromyographic patterns of masticatory and cervical spine muscles in emmetropic Caucasian subjects by gender. (2) Methods: After applying the inclusion criteria, 50 emmetropic Caucasian subjects were included in the study. Four muscle pairs were analyzed: the temporalis muscle (TA), the masseter muscle (MM), the digastric muscle (DA), and the sternocleidomastoid muscle (SCM), during resting and functional activity. (3) Results: It was observed that there were no significant statistical differences in activity and bioelectrical patterns between open and closed eyes in women and men, with the exception of clenching on dental cotton rollers in DA-left and DA mean between tests in women. The observed statistical results had a small effect size, successively equal to 0.32 and 0.29. (4) Conclusions: Changes in the influence of visual input do not affect electromyographic activity and patterns of masticatory and cervical spine muscles in emmetropic Caucasian women and men.