Electromyography; sEMG

This study explores the potential of electromyography (EMG) decoding to enhance motor function outcomes, focusing on developing an innovative EMG-based hand movement classification system. Leveraging advanced signal processing and machine learning techniques, our objectives are twofold: (1) optimize EMG decoding performance through time-domain windowing, feature selection, and classifier optimization, and (2) assess the system's effectiveness in classifying 15 distinct finger movements.

This dataset comprises data from six experimental participants, each undergoing nine walking trials. Each participant engaged in three trials of low-speed walking, three trials of medium-speed walking, and three trials of high-speed walking. The dataset includes multi-channel electromyography (EMG) data and center of pressure/ground reaction force (COP/GRF) data. Specifically, EMG data is utilized to extract muscle coordination activation time coefficients during human walking, and a deep learning model is established based on these coefficients to predict COP/GRF parameters.

This dataset contains the simutaneously acquired sEMG and EEG signals when 8 subjects performing hand motions.

Recently, surface electromyography (sEMG) emerged as a novel biometric authentication method. Since EMG system parameters, such as the feature extraction methods and the number of channels, have been known to affect system performances, it is important to investigate these effects on the performance of the sEMG-based biometric system to determine optimal system parameters.