Dataset for multi-channel surface electromyography (sEMG) signals of hand gestures

This dataset contains electromyography (EMG) signals for use in human-computer interaction studies. The dataset includes 4-channel surface EMG data from 40 participants with an equal gender distribution. The gestures in the data are rest or neutral state, extension of the wrist, flexion of the wrist, ulnar deviation of the wrist, radial deviation of the wrist, grip, abduction of all fingers, adduction of all fingers, supination, and pronation. Data were collected from 4 forearm muscles when simulating 10 unique hand gestures and recorded with the BIOPAC MP36 device using Ag/AgCl surface bipolar electrodes. Each participant's data contains five repetitive cycles of ten hand gestures. A demographic survey was applied to the participants before the signal recording process. This data can be utilized for recognition, classification, and prediction studies in order to develop EMG-based hand movement controller systems. The dataset can also be useful as a reference to create an artificial intelligence model (especially a deep learning model) to detect gesture-related EMG signals. Additionally, it is encouraged to use the proposed dataset for benchmarking of current datasets or for validation of machine learning and deep learning models created with different datasets in accordance with the participant-independent validation strategy.本数据集包含肌电信号（electromyography, EMG），可应用于人机交互相关研究。数据集涵盖40名参与者的4通道表面肌电数据，参与者性别分布均衡。数据包含的手势动作包括静息或中立状态、手腕背伸、手腕掌屈、手腕尺偏、手腕桡偏、握拳、全指外展、全指内收、前臂旋后以及前臂旋前。数据采集自4块前臂肌肉，在模拟10种独特手部手势的过程中进行采集，并通过BIOPAC MP36设备结合Ag/AgCl表面双极电极完成记录。每名参与者的数据包含10种手部手势各5次重复循环的采样记录。在信号录制流程开始前，已对所有参与者进行人口统计学问卷调查。该数据集可用于识别、分类与预测相关研究，以开发基于肌电信号的手部运动控制系统。此外，本数据集还可作为参考，用于构建用于检测手势相关肌电信号的人工智能模型（尤其是深度学习模型）。同时，鼓励使用本数据集开展现有数据集的基准测试工作，或依据参与者独立验证策略，对使用其他数据集构建的机器学习与深度学习模型进行验证。