An important challenge in the study of functional corticomuscular coupling (FCMC) is an accurate capture of the coupling relationship between the cerebral cortex and the effector muscle. The coherence method is a linear analysis method, which has certain limitations in further revealing the nonlinear coupling between neural signals. Although mutual information (MI) and transfer entropy (TE) based on information theory can capture both linear and nonlinear correlations, the equitability of these algorithms is ignored and the nonlinear components of the correlation cannot be separated. The maximal information coefficient (MIC) is a suitable method to measure the coupling between neurophysiological signals. This study extends the MIC to the time–frequency domain, named time–frequency maximal information coefficient (TFMIC), to explore the FCMC in a specific frequency band. The effectiveness, equitability, and robustness of the algorithm on the simulation data was verified and compared with coherence, TE- and MI- based methods. Simulation results showed that the TFMIC could accurately detect the coupling for different functional relationships at low noise levels. The dorsiflexion experimental results revealed that the beta-band (14–30 Hz) significant coupling was observed at channels Cz, C4, FC4, and FCz. Additionally, the results showed that the coupling was higher in the alpha-band (8– 13 Hz) and beta-band (14–30 Hz) than in the gamma-band (31–45 Hz). This might be related to a transition between sensorimotor states. Specifically, the nonlinear component of FCMC was also observed at channels Cz, C4, FC4, and FCz. This study expanded the research on nonlinear coupling components in FCMC.

Gait movement is an important activity in daily human life. The coordination of gait movement is directly affected by the cooperation and functional connectivity between muscles. However, the mechanisms of muscle operation at different gait speeds remain unclear. Therefore, this study addressed the gait speed effect on the changes in cooperative modules and functional connectivity between muscles. To this end, surface electromyography (sEMG) signals were collected from eight key lower extremity muscles of twelve healthy subjects walking on a treadmill at high, middle, and low motion speeds. Nonnegative matrix factorization (NNMF) was applied to the sEMG envelope and intermuscular coherence matrix, yielding five muscle synergies. Muscle functional networks were constructed by decomposing the intermuscular coherence matrix, revealing different layers of functional muscle networks across frequencies. In addition, the coupling strength between cooperative muscles grew with gait speed. Different coordination patterns among muscles with changes in gait speed related to the neuromuscular system regulation were identified.

Objective. The combination of the motor imagery (MI) electroencephalography (EEG) signals and deep learning-based methods is an effective way to improve MI classification accuracy. However, deep learning-based methods often need too many trainable parameters. As a result, the trade-off between the network decoding performance and computational cost has always been an important challenge in the MI classification research. Approach. In the present study, we proposed a new end-to-end convolutional neural network (CNN) model called the EEG-circular dilated convolution (CDIL) network, which takes into account both the lightweight model and the classification accuracy. Specifically, the depth-separable convolution was used to reduce the number of network parameters and extract the temporal and spatial features from the EEG signals. CDIL was used to extract the time-varying deep features that were generated in the previous stage. Finally, we combined the features extracted from the two stages and used the global average pooling to further reduce the number of parameters, in order to achieve an accurate MI classification. The performance of the proposed model was verified using three publicly available datasets. Main results. The proposed model achieved an average classification accuracy of 79.63% and 94.53% for the BCIIV2a and HGD four-classification task, respectively, and 87.82% for the BCIIV2b two-classification task. In particular, by comparing the number of parameters, computation and classification accuracy with other lightweight models, it was confirmed that the proposed model achieved a better balance between the decoding performance and computational cost. Furthermore, the structural feasibility of the proposed model was confirmed by ablation experiments and feature visualization. Significance. The results indicated that the proposed CNN model presented high classification accuracy with less computing resources, and can be applied in the MI classification research.

Falls are the major cause of fatal and non-fatal injury among people aged more than 65 years. Due to the grave consequences of the occurrence of falls, it is necessary to conduct thorough research on falls. This paper presents a method for the study of fall detection using surface electromyography (sEMG) based on an improved dual parallel channels convolutional neural network (IDPC-CNN). The proposed IDPC-CNN model is designed to identify falls from daily activities using the spectral features of sEMG. Firstly, the classification accuracy of time domain features and spectrograms are compared using linear discriminant analysis (LDA), k-nearest neighbor (KNN) and support vector machine (SVM). Results show that spectrograms provide a richer way to extract pattern information and better classification performance. Therefore, the spectrogram features of sEMG are selected as the input of IDPC-CNN to distinguish between daily activities and falls. Finally, The IDPC-CNN is compared with SVM and three different structure CNNs under the same conditions. Experimental results show that the proposed IDPC-CNN achieves 92.55% accuracy, 95.71% sensitivity and 91.7% specificity. Overall, The IDPC-CNN is more effective than the comparison in accuracy, efficiency, training and generalization.

How to effectively detect muscle compensation caused by exercise fatigue has attracted increasing attention during the last decade, with most previous work being focused on the research of compensatory movement strategy, which indirectly reflecting muscle compensation through the analysis of kinematic data. However, relatively few investigations can be found in the literature for using muscle information to analyze muscle compensation directly. This paper focuses on the muscle compensation analysis based on a 8-channel surface electromyography (sEMG). We investigate the application of complex networks theory in kinematic analysis. Motivated by the good performance of complex networks, we further propose designing the muscle functional network based on sEMG to analyze muscle compensation during exercise, and extract the network features that can reflect the changes of muscle compensation are extracted from the network. Real results from twelve subjects show that muscle functional network can effectively reflect the changes of muscle relationship caused by exercise-induced fatigue. The feature extracted from the muscle network has a high correlation with the feature of muscle fatigue ( $r_{\text{ACC-MDF}}:0.7157$ , $r_{\text{AGE-RMS}}:0.7877$ ), and can be used as a reliable index to analyze muscle compensation changes.

Intelligent prosthetic hand is an important branch of intelligent robotics. It can remotely replace humans to complete various complex tasks and also help humans to complete rehabilitation training. In human-computer interaction technology, the prosthetic hand can be accurately controlled by surface electromyography (sEMG). This paper proposes a new multichannel fusion scheme (MSFS) to extend the virtual channels of sEMG and improve the accuracy of gesture recognition. In addition, the Temporal Convolutional Network (TCN) in deep learning has been improved to enhance the performance of the network. Finally, the sEMG is collected by the Myo armband and the prosthetic hand is controlled in real time to validate the new method. The experimental results show that the method proposed in this paper can improve the accuracy of the control intelligent prosthetic hand, and the accuracy rate is 93.69%.