Electromyography Signals Research Articles

Intelligent Human-Computer Interaction: Combined Wrist and Forearm Myoelectric Signals for Handwriting Recognition.

Recent studies have highlighted the possibility of using surface electromyographic (EMG) signals to develop human-computer interfaces that are also able to recognize complex motor tasks involving the hand as the handwriting of digits. However, the automatic recognition of words from EMG information has not yet been studied. The aim of this study is to investigate the feasibility of using combined forearm and wrist EMG probes for solving the handwriting recognition problem of 30 words with consolidated machine-learning techniques and aggregating state-of-the-art features extracted in the time and frequency domains. Six healthy subjects, three females and three males aged between 25 and 40 years, were recruited for the study. Two tests in pattern recognition were conducted to assess the possibility of classifying fine hand movements through EMG signals. The first test was designed to assess the feasibility of using consolidated myoelectric control technology with shallow machine-learning methods in the field of handwriting detection. The second test was implemented to assess if specific feature extraction schemes can guarantee high performances with limited complexity of the processing pipeline. Among support vector machine, linear discriminant analysis, and K-nearest neighbours (KNN), the last one showed the best classification performances in the 30-word classification problem, with a mean accuracy of 95% and 85% when using all the features and a specific feature set known as TDAR, respectively. The obtained results confirmed the validity of using combined wrist and forearm EMG data for intelligent handwriting recognition through pattern recognition approaches in real scenarios.

How to use one surface electromyography sensor to recognize six hand movements for a mechanical hand in real time: a method based on Morse code.

Surface electromyography (sEMG) signal is a kind of physiological signal reflecting muscle activity and muscle force. At present, the existing methods of recognizing human motion intention need more than two sensors to recognize more than two kinds of movements, the sensor pasting positions are special, and the hardware conditions for execution are high. In this work, a real-time motion intention recognition method based on Morse code is proposed and applied to the mechanical hand. The short-time and long-term muscle contraction signals collected by a single sEMG sensor were extracted and encoded with the Morse code method, and then the developed mapping method from Morse code to six hand movements were used to recognize hand movements. The average recognition accuracy of hand movements was 94.8704 ± 2.3556%, the average adjusting time was 34.89s for all subjects, and the execution time of a single movement was 381ms. The corresponding experiment video can be found in the attachment to show the experiment. The method proposed in this work is a method with one sensor to recognize six movements, low hardware conditions, high recognition accuracy, and insensitive to the difference of sensor pasting position.

Reduced amplitude and slowed latency of the acoustic startle response in adolescents and adults with 22q11.2 deletion syndrome

Background22q11.2 deletion syndrome (22q11DS) is one of the most robust genetic predictors of psychosis and other psychiatric illnesses. In this study, we examined 22q11DS subjects' acoustic startle responses (ASRs), which putatively index psychosis risk. Latency of the ASR is a presumptive marker of neural processing speed and is prolonged (slower) in schizophrenia. ASR measures correlate with increased psychosis risk, depend on glutamate and dopamine receptor signaling, and could serve as translational biomarkers in interventions for groups at high psychosis risk. MethodsStartle magnitude, latency, and prepulse inhibition were assessed with a standard acoustic startle paradigm in 31 individuals with 22q11.2DS and 32 healthy comparison (HC) subjects. Surface electrodes placed on participants' orbicularis oculi recorded the electromyographic signal in ASR eyeblinks. Individuals without measurable startle blinks in the initial habituation block were classified as non-startlers. ResultsAcross the startle session, the ASR magnitude was significantly lower in 22q11DS subjects than HCs because a significantly higher proportion of 22q11DS subjects were non-startlers. Latency of the ASR to pulse-alone stimuli was significantly slower in 22q11DS than HC subjects. Due to the overall lower 22q11DS startle response frequency and magnitudes prepulse inhibition could not be analyzed. ConclusionsReduced magnitude and slow latency of 22q11DS subjects' responses suggest reduced central nervous system and neuronal responsiveness. These findings are consistent with significant cognitive impairments observed in 22q11DS subjects. Further research is needed to untangle the connections among basic neurotransmission dysfunction, psychophysiological responsiveness, and cognitive impairment.

A novel balance index for preliminary assessment of proprioception based on a balance-cognitive study

Finding a quick and accurate method for evaluating proprioception is useful for improving performance which is the main objective of the current research. This paper presents a novel index, called the Synergy Balance Index (SBI), that leverages lower limb electromyography signals for the evaluation of proprioception. The concept has been tested on a set of balance tasks performed by 23 healthy subjects on a ∞-shape path. The capability of the SBI has been investigated using two ways, classification and monitoring. Also, the relationship between the features extracted from the recorded signals was investigated by calculating the correlation coefficient. The findings suggest that in the absence of sufficient and accurate information on individuals, this index can only differentiate groups if there is a significant difference in their proprioception. All of the evaluated criteria were obtained above 95 percent. This index was effective when used with equal conditions and comparisons of each individual with himself over multiple stages of proprioception training that included two tasks (balance, and cognitive). The index showed increased proprioception performance after just one training session, showing its possible sensitivity and speed. The SBI is a quick, low-cost, and easy-to-use index for assessing a person’s proprioception. It has potential applications in rehabilitation, neurology, and sports biology. Enhanced research in this area could result in better patient care. The small sample size and the lack of a diverse population group limit the study. This can be fixed by gathering more data from a larger and more diverse set of participants.

Highly stretchable and customizable microneedle electrode arrays for intramuscular electromyography.

Stretchable three-dimensional (3D) penetrating microelectrode arrays have potential utility in various fields, including neuroscience, tissue engineering, and wearable bioelectronics. These 3D microelectrode arrays can penetrate and conform to dynamically deforming tissues, thereby facilitating targeted sensing and stimulation of interior regions in a minimally invasive manner. However, fabricating custom stretchable 3D microelectrode arrays presents material integration and patterning challenges. In this study, we present the design, fabrication, and applications of stretchable microneedle electrode arrays (SMNEAs) for sensing local intramuscular electromyography signals ex vivo. We use a unique hybrid fabrication scheme based on laser micromachining, microfabrication, and transfer printing to enable scalable fabrication of individually addressable SMNEA with high device stretchability (60 to 90%). The electrode geometries and recording regions, impedance, array layout, and length distribution are highly customizable. We demonstrate the use of SMNEAs as bioelectronic interfaces in recording intramuscular electromyography from various muscle groups in the buccal mass of Aplysia.

MVF-SleepNet: Multi-View Fusion Network for Sleep Stage Classification.

Sleep stage classification is of great importance in human health monitoring and disease diagnosing. Clinically, visual-inspected classifying sleep into different stages is quite time consuming and highly relies on the expertise of sleep specialists. Many automated models for sleep stage classification have been proposed in previous studies but their performances still exist a gap to the real clinical application. In this work, we propose a novel multi-view fusion network named MVF-SleepNet based on multi-modal physiological signals of electroencephalography (EEG), electrocardiography (ECG), electrooculography (EOG), and electromyography (EMG). To capture the relationship representation among multi-modal physiological signals, we construct two views of Time-frequency images (TF images) and Graph-learned graphs (GL graphs). To learn the spectral-temporal representation from sequentially timed TF images, the combination of VGG-16 and GRU networks is utilized. To learn the spatial-temporal representation from sequentially timed GL graphs, the combination of Chebyshev graph convolution and temporal convolution networks is employed. Fusing the spectral-temporal representation and spatial-temporal representation can further boost the performance of sleep stage classification. A large number of experiment results on the publicly available datasets of ISRUC-S1 and ISRUC-S3 show that the MVF-SleepNet achieves overall accuracy of 0.821, F1 score of 0.802 and Kappa of 0.768 on ISRUC-S1 dataset, and accuracy of 0.841, F1 score of 0.828 and Kappa of 0.795 on ISRUC-S3 dataset. The MVF-SleepNet achieves competitive results on both datasets of ISRUC-S1 and ISRUC-S3 for sleep stage classification compared to the state-of-the-art baselines. The source code of MVF-SleepNet is available on Github (https://github.com/YJPai65/MVF-SleepNet).

Are performance and perceived fatigability dependent on the anchor scheme of fatiguing isometric tasks in men?

Ratings of perceived exertion (RPE) can be used to regulate exercise intensity. This study examined the effect of anchor scheme on performance fatigability and neuromuscular responses following fatiguing forearm flexion tasks. Twelve men (age 20.9±2.2 years; height 179.8±5.3 cm; body mass 80.2±9.9 kg) performed sustained, isometric forearm flexion tasks to failure anchored to RPE=6 (RPEFT) and the torque (TRQFT) that corresponded to RPE=6. Pre-test and post-test maximal voluntary isometric contractions (MVIC) were performed to quantify changes in the amplitude (AMP) and mean power frequency (MPF) of the electromyographic (EMG) and mechanomyographic (MMG) signals. Neuromuscular efficiency (NME) was calculated by dividing normalized torque by normalized EMG AMP. A dependent t-test was used to assess the mean difference for time to task failure (TTF). Repeated measures ANOVAs were used to compare mean differences for performance fatigability and normalized neuromuscular parameters. The RPEFT had a greater TTF than the TRQFT (P<0.001). MVIC and NME decreased from pre-test to post-test following the RPEFT and TRQFT (P<0.05) with no differences between anchor schemes. Following the TRQFT, normalized EMG MPF decreased from pre-test to post-test (P=0.004). Following the RPEFT, normalized MMG MPF increased from pre-test to post-test (P=0.021). There were no changes in normalized EMG AMP or MMG AMP (P>0.05). These findings indicated anchor scheme-specific neuromuscular responses and TTF, despite no difference in performance fatigability. Furthermore, performance fatigability was likely due to peripheral fatigue (based on normalized EMG MPF and NME) following the TRQFT, but peripheral and central fatigue (based on normalized MMG MPF and NME) following the RPEFT.

The Effects of Anchoring a Fatiguing Forearm Flexion Task to a High vs. Low Rating of Perceived Exertion on Torque and Neuromuscular Responses.

Ortega, DG, Housh, TJ, Smith, RW, Arnett, JE, Neltner, TJ, Schmidt, RJ, and Johnson, GO. The effects of anchoring a fatiguing forearm flexion task to a high versus low rating of perceived exertion on torque and neuromuscular responses. J Strength Cond Res 38(5): e219-e225, 2024-This study examined the torque and neuromuscular responses following sustained, isometric, forearm flexion tasks anchored to 2 ratings of perceived exertion (RPE). Nine men (mean ± SD: age = 21.0 ± 2.4 years; height = 179.5 ± 5.1 cm; body mass = 79.6 ± 11.4 kg) completed maximal voluntary isometric contractions (MVIC) before and after sustained, isometric, forearm flexion tasks to failure anchored to RPE = 2 and RPE = 8. The amplitude (AMP) and mean power frequency (MPF) of the electromyographic (EMG) signal were recorded from the biceps brachii. Normalized torque was divided by normalized EMG AMP to calculate neuromuscular efficiency (NME). A dependent t-test was used to assess the mean difference for time to task failure (TTF). Repeated-measures analysis of variances was used to compare mean differences for MVIC and normalized neuromuscular parameters. There was no significant difference in TTF between RPE = 2 and RPE = 8 (p = 0.713). The MVIC decreased from pretest to posttest at RPE = 2 (p = 0.009) and RPE = 8 (p = 0.003), and posttest MVIC at RPE = 8 was less than that at RPE = 2 (p < 0.001). In addition, NME decreased from pretest to posttest (p = 0.008). There was no change in normalized EMG AMP or EMG MPF (p > 0.05). The current findings indicated that torque responses were intensity specific, but TTF and neuromuscular responses were not. Furthermore, normalized EMG AMP and EMG MPF remained unchanged but NME decreased, likely due to peripheral fatigue and excitation-contraction coupling failure. Thus, this study provides information regarding the neuromuscular responses and mechanisms of fatigue associated with tasks anchored to RPE, which adds to the foundational understanding of the relationship between resistance exercise and the perception of fatigue.

Neuromuscular Adaptations in Response to Disuse and Weight-Training Rehabilitation are Enhanced in Resistance Trained Individuals

INTRODUCTION: Lower-limb disuse from immobilization results in a decrease in muscle strength, volume, and function. However, muscle atrophy from disuse does not entirely account for the observed strength and functional declines. Additionally, prior engagement in resistance training (RT) has been linked to the preservation of muscular strength and neuromuscular function. Therefore, the purpose of this study was to assess the influence of a single-leg immobilization protocol, followed by structured RT rehabilitation protocol, on muscle strength, volume, and neural activation in healthy young adults with and without previous strength training experience. Methods: Participants (10 Trained (T): 8 men, 2 women; 11 Untrained (UT): 8 men, 3 women) underwent 2-weeks of left-knee immobilization at 90° knee-flexion with a hinged immobilizer knee-brace. Participants were given a pair of axillary crutches for ambulation during this time. Following immobilization, all participants underwent an 8-week structured RT rehabilitation protocol. Participants visited the laboratory for three testing sessions (baseline test on Day 0; post-immobilization at the end of week 2; and post-RT rehab at the end of week 10). Maximal knee extensor isometric strength at 45°was quantified using an isokinetic dynamometer (Biodex). Voluntary muscle activation was quantified as the root mean square (RMS) of the interference electromyography (EMG) signals derived from two indwelling fine-wire electrodes in the vastus-lateralis muscle. Additionally, a continuous wavelet transform (CWT) was performed on the EMG to determine amplitude versus frequency characteristics of the signals and how this amplitude varied with time in the 13-30 (beta) and 30-60 (piper) Hz bands. Mid-thigh muscle cross-sectional area (mCSA) and density were also quantified using peripheral quantitative computed tomography (pQCT) during all testing sessions. Results: The T group was significantly stronger (p=0.019), had greater muscle density (p=0.005) and mid-thigh mCSA (p&lt;0.001) across all sessions when compared with the UT group. However, there were no significant time or interaction effects for strength or muscle density losses (p&gt;0.05). While both groups demonstrated increased muscle activation across sessions (RMS: p=0.04; CWT: p=0.021), the T group demonstrated a greater increase in the neural drive from baseline to week 10 when compared with the UT group (RMS: p= 0.002; CWT: p=0.027). CONCLUSION: In younger adults with or without prior strength training experience, we find that losses in knee extensor isometric strength, muscle density, and mid-thigh mCSA are minimal following a 2-week immobilization protocol. However, certain neuromuscular adaptations during immobilization and following weight-training rehabilitation are enhanced to a greater degree in those with prior training, which is seemingly indicative of greater synchrony in descending cortical drive and motor unit recruitment. These latter findings warrant continued investigation using additional innovative methodologies (e.g., fMRI, TMS, etc.) for assessing the mechanisms underlying the effects of prehab protocols on neuromuscular adaptations. PGRF and Nix family foundation scholarship to Marina Meyer. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Developing RPC-Net: Leveraging High-Density Electromyography and Machine Learning for Improved Hand Position Estimation.

The purpose of this study was to develop and evaluate the performance of RPC-Net (Recursive Prosthetic Control Network), a novel method using simple neural network architectures to translate electromyographic activity into hand position with high accuracy and computational efficiency. RPC-Net uses a regression-based approach to convert forearm electromyographic signals into hand kinematics. We tested the adaptability of the algorithm to different conditions and compared its performance with that of solutions from the academic literature. RPC-Net demonstrated a high degree of accuracy in predicting hand position from electromyographic activity, outperforming other solutions with the same computational cost. Including previous position data consistently improved results across subjects and conditions. RPC-Net showed robustness against a reduction in the number of electromyography electrodes used and shorter input signals, indicating potential for further reduction in computational cost. The results demonstrate that RPC-Net is capable of accurately translating forearm electromyographic activity into hand position, offering a practical and adaptable tool that may be accessible in clinical settings. The development of RPC-Net represents a significant advancement. In clinical settings, its application could enable prosthetic devices to be controlled in a way that feels more natural, improving the quality of life for individuals with limb loss.

Online Muscle Activation Onset Detection Using Likelihood of Conditional Heteroskedasticity of Electromyography Signals.

The real-time, accurate detection of muscle activation onset (MAO) is significant for EMG-triggered control strategies in embedded applications like prostheses and exoskeletons. This paper investigates sEMG signals using the generalized autoregressive conditional heteroskedasticity (GARCH) model, focusing on variance. A novel feature, the likelihood of conditional heteroskedasticity (LCH) extracted from the maximum likelihood estimation of GARCH parameters, is proposed. This feature effectively distinguishes signal from noise based on heteroskedasticity, allowing for the detection of MAO through the LCH feature and a basic threshold classifier. For online calculation, the model parameter estimation is simplified, enabling direct calculation of the LCH value using fixed parameters. The proposed method was validated on two open-source datasets and demonstrated superior performance over existing methods. The mean absolute error of onset detection, compared with visual detection results, is approximately 65 ms under online conditions, showcasing high accuracy, universality, and noise insensitivity. The results indicate that the proposed method using the LCH feature from the GARCH model is highly effective for real-time detection of muscle activation onset in sEMG signals. This novel approach shows great potential and possibility for real-world applications, reflecting its superior performance in accuracy, universality, and insensitivity to noise.

Spectral characterization of human leg EMG signals from an open access dataset for the development of computational models.

Large-scale neuromusculoskeletal models have been used for predicting mechanisms underlying neuromuscular functions in humans. Simulations of such models provide several types of signals of practical interest, such as surface electromyographic signals (EMG), which are compared with experimental data for interpretations of neurophysiological phenomena under study. Specifically, realistic characterization of spectral properties of simulated EMG signals is important for achieving powerful inferences, whereas considerations should be taken for myoelectric signals of different muscles. In this study, we characterized spectral properties of surface interference pattern EMG signals and motor unit action potentials (MUAP) acquired from three plantar flexor muscles: Soleus (SO), Medial Gastrocnemius (MG), and Lateral Gastrocnemius (LG); and one dorsiflexor muscle: Tibialis Anterior (TA). Surface EMG signals were acquired from 20 participants using the same convention for electrode placement. Specifically, interference pattern EMG signals were obtained during isometric constant force contractions at 5%, 10% and 20% of maximum voluntary contraction (MVC), whereas surface MUAPs were decomposed from surface EMG signals obtained at low contraction forces. We compared the spectrum median frequency (MDF) estimated from interference pattern EMG signals across muscles and contraction intensities. Additionally, we compared MDF and durations of MUAPs between muscles. Our results showed that MDF of interference pattern EMG signals acquired from TA were higher compared to SO, MG, and LG for all contraction intensities i.e., 5%, 10%, and 20% MVC. Consistently, MUAPs acquired from TA also had higher MDF values and shorter durations compared to the other leg muscles. We provide herein a dataset with the surface MUAPs waveforms and interference pattern EMG signals obtained for this study, which should be useful for implementing and validating the simulation of myoelectrical signals of leg muscles. Importantly, these results indicate that spectral properties of myoelectrical signals should be considered for improving EMG modeling in large-scale neuromusculoskeletal models.

Mapping Method of Human Arm Motion Based on Surface Electromyography Signals.

This paper investigates a method for precise mapping of human arm movements using sEMG signals. A multi-channel approach captures the sEMG signals, which, combined with the accurately calculated joint angles from an Inertial Measurement Unit, allows for action recognition and mapping through deep learning algorithms. Firstly, signal acquisition and processing were carried out, which involved acquiring data from various movements (hand gestures, single-degree-of-freedom joint movements, and continuous joint actions) and sensor placement. Then, interference signals were filtered out through filters, and the signals were preprocessed using normalization and moving averages to obtain sEMG signals with obvious features. Additionally, this paper constructs a hybrid network model, combining Convolutional Neural Networks and Artificial Neural Networks, and employs a multi-feature fusion algorithm to enhance the accuracy of gesture recognition. Furthermore, a nonlinear fitting between sEMG signals and joint angles was established based on a backpropagation neural network, incorporating momentum term and adaptive learning rate adjustments. Finally, based on the gesture recognition and joint angle prediction model, prosthetic arm control experiments were conducted, achieving highly accurate arm movement prediction and execution. This paper not only validates the potential application of sEMG signals in the precise control of robotic arms but also lays a solid foundation for the development of more intuitive and responsive prostheses and assistive devices.

Rectified Latent Variable Model-Based EMG Factorization of Inhibitory Muscle Synergy Components Related to Aging, Expertise and Force-Tempo Variations.

Muscle synergy has been widely acknowledged as a possible strategy of neuromotor control, but current research has ignored the potential inhibitory components in muscle synergies. Our study aims to identify and characterize the inhibitory components within motor modules derived from electromyography (EMG), investigate the impact of aging and motor expertise on these components, and better understand the nervous system's adaptions to varying task demands. We utilized a rectified latent variable model (RLVM) to factorize motor modules with inhibitory components from EMG signals recorded from ten expert pianists when they played scales and pieces at different tempo-force combinations. We found that older participants showed a higher proportion of inhibitory components compared with the younger group. Senior experts had a higher proportion of inhibitory components on the left hand, and most inhibitory components became less negative with increased tempo or decreased force. Our results demonstrated that the inhibitory components in muscle synergies could be shaped by aging and expertise, and also took part in motor control for adapting to different conditions in complex tasks.

Biometric Authentication Based on EMG Hand Gestures Signals Using CNN

Biometric identification systems are increasingly important today compared to traditional recognition/classification systems. Electromyography (EMG) signals and person identification/classification systems are preferred for high-security systems as they include physiological and behavioural movements. This study investigates biometric EMG signals based on convolutional neural networks (CNNs) and personal identification/classification systems. Bioelectric signals were recorded at six different wrist movements from five volunteer participants with a four-channel EMG device. To determine the spectrum characteristics of EMG signals, the frequency subbands of the signals were found using the discrete wavelet transform (DWT), empirical wavelet transform (EWT), and empirical mode decomposition (EMD) methods. In addition, statistical methods are used to improve the effectiveness of the feature vector. The CNN model was used to define or classify people. The performance of the developed system was evaluated using Accuracy, Precision, Sensitivity, F-score parameters. As a result, a classification success of 95.66 % was achieved with the developed EMD-CNN method, 94.10 % with the DWT-CNN method, and 93.33 % with the EWT-CNN method. The artificial intelligence model presented in this study explains the effectiveness of EMG signals in person recognition or classification as a biometric identification system. Furthermore, the developed model shows promise for the development and design of future biometric recognition systems.

Nonlinear dynamics of postural control system under visual-vestibular habituation balance practice: evidence from EEG, EMG and center of pressure signals.

Human postural control system is inherently complex with nonlinear interaction among multiple subsystems. Accordingly, such postural control system has the flexibility in adaptation to complex environments. Previous studies applied complexity-based methods to analyze center of pressure (COP) to explore nonlinear dynamics of postural sway under changing environments, but direct evidence from central nervous system or muscular system is limited in the existing literature. Therefore, we assessed the fractal dimension of COP, surface electromyographic (sEMG) and electroencephalogram (EEG) signals under visual-vestibular habituation balance practice. We combined a rotating platform and a virtual reality headset to present visual-vestibular congruent or incongruent conditions. We asked participants to undergo repeated exposure to either congruent (n = 14) or incongruent condition (n = 13) five times while maintaining balance. We found repeated practice under both congruent and incongruent conditions increased the complexity of high-frequency (0.5-20 Hz) component of COP data and the complexity of sEMG data from tibialis anterior muscle. In contrast, repeated practice under conflicts decreased the complexity of low-frequency (<0.5 Hz) component of COP data and the complexity of EEG data of parietal and occipital lobes, while repeated practice under congruent environment decreased the complexity of EEG data of parietal and temporal lobes. These results suggested nonlinear dynamics of cortical activity differed after balance practice under congruent and incongruent environments. Also, we found a positive correlation (1) between the complexity of high-frequency component of COP and the complexity of sEMG signals from calf muscles, and (2) between the complexity of low-frequency component of COP and the complexity of EEG signals. These results suggested the low- or high-component of COP might be related to central or muscular adjustment of postural control, respectively.

The phenomenon of anticipation in fencing. An applicability approach.

The aim of the study was to determine the structure of muscular activity and ground reaction forces during the preparatory period and the execution of a fencing lunge at the opponent's torso. The analysis focused on the correlations between three phases of a fencing technical action in the context of factors of temporal anticipation. Six female épée fencers from the Polish National Fencing Team participated in the study. The research tools included electromyography (EMG), ground reaction force (GRF) platforms, and the OptiTrack motion capture system. The fencers performed the lunge three times in response to visual cues from the coach. By integrating the testing system, the EMG signal indices of the fencers' upper and lower limbs and the vertical force values of the fencers' front and rear leg muscles were obtained simultaneously. The results of the study demonstrated the key role of five muscles: BICEPS BRACHII, LAT TRICEPS, EXTCARP RAD, BICEPS FEMORIS and MED GAS in influencing the speed of lunge execution. In addition, a significant correlation was found between the EMG signal of the gastrocnemius muscle of the rear leg and the movement time (MT) phase of the lunge execution. The anticipatory activation of the EMG signal in relation to the vertical force waveforms generated by the ground forces response platform in the 15-30 ms interval was demonstrated. Finally, the importance of the preparatory period for the effectiveness of the fencing lunge was highlighted based on the phenomenon of anticipation.

An On‐Skin‐Formed Silk Protein Bioelectrode for Conformable and Robust Electrophysiological Interface

AbstractThe electrophysiological interface is essential for understanding electrical activities within biological organisms and human‐machine interaction. However, traditional preformed bioelectrodes show insufficient interface contact with hairy and irregular skin, causing signal attenuation and distortion. Here, an on‐skin‐formed silk protein bioelectrode is reported with a conformable and robust interface, achieving a high signal‐to‐noise ratio for electromyographic signals, showing up to a 38.9% increase compared to traditional ones. The fluid‐gel transition of silk protein is induced by a gelling agent composed of ethanol and sodium alginate, leading to a conformable and seamless interface with hairy and irregular human skin, thus resulting in a high signal‐to‐noise ratio. The developed bioelectrode can record electrophysiological signals stably, even underwater. Additionally, human‐machine interaction using electromyographic signals is designed to precisely control the model car's motion trajectory. This work presents an effective method for developing a seamless and conformable electrophysiological interface in bioelectronics.

A reusable, healable, and biocompatible PEDOT:PSS hydrogel-based electrical bioadhesive interface for high-resolution electromyography monitoring and time–frequency analysis

As the most ideal interface between bioelectronic devices and biological tissues, the electrical bioadhesive interface (EBI) holds significant potential for various applications, including soft bioelectronics, wearable devices, and electrophysiological signal monitoring. However, most current EBIs are disposable products that lack repeatable adhesion and self-healing properties, thus hindering practical applications. Herein, we develop a reusable EBI by incorporating a dynamic non-covalent interaction enhancer polydopamine (PDA) and conductive PEDOT:PSS into an adhesive hydrogel matrix polyacrylamide (PAM). The EBI exhibits intriguing overall electrical and mechanical properties, including high conductivity (5.57 S m−1), tissue-like Young’s modulus (2.7 kPa), low interfacial impedance (1 kΩ), and excellent adhesion strength (46.45 ± 1.75 kPa on porcine skin) with excellent retention (87 %) after 200 adhering/detaching cycles, high self-healing efficiency of 94.11 %, and outstanding biocompatibility in HDF-a and HaCaT cell models. Benefiting from these superior properties, we further demonstrate the fabrication and application of an EBI-based reusable electromyography monitoring patch (EMP). The reusable EMP enables high-resolution electromyography signal monitoring capability with a higher signal-to-noise ratio (SNR) of 30.05 dB than commercial electrodes (12.06 dB). Remarkably, such reusable EMP maintains very stable SNR of 21.82 dB after 200 adhering/detaching cycles (3.8 times higher than commercial electrodes, 5.74 dB after 30 cycles), and showcases typical time and frequency-domain characteristics. These findings demonstrate a promising reusable EMP to enhance the accuracy and convenience of electromyography monitoring.

The comparison and processing of Electromyography (EMG) signals under different actions

The experiment first investigates the working principle and the applications of Electromyography (EMG). Then, the effect of electrode location and electrode size on the EMG signal is explored by the raw data and theory. The reasons for choosing generic surface Ag/AgCl adhesive electrodes are also described. After obtaining the raw EMG signals, suitable data processing methods, such as Root Mean Square (RMS) and digital filtering are applied to generate signals that can drive the motor of a prosthetic arm. The results obtained by the two methods are also compared, where RMS can provide larger peak and mean values, and the digital filtering method can minimise the phase shift. Finally, the limitations of the EMG for prostheses are also analysed by three aspects: different flexion forces, individual finger motion and different reference locations.