HD-EMG signals and corresponding neural drive signals for training and validation of a deep CNN for neural drive estimation across muscles and participants

HD-EMG signals and corresponding neural drive signals, in form of composite spike train (CST), for training and validation of a deep CNN framework for neural drive estimation. <br> We included processed data from five participants (uc4, uc8, uc11, uc25, uc26), each with two muscles [vastus lateralis (VL) and the vastus medialis (VM) ] across two sessions (d1 and d2). In each session, they performed three isometric contractions at 20% maximum voluntary contraction. For detailed information, please refer to our manuscript: https://doi.org/10.1101/2022.08.31.505855. The raw HD-EMG and corresponding CST are captured at 2048 Hz. We used a window length of 40 data points and sliding step of 20 data points with an overlap of 20 data points to segment the HD-EMG signals, and we used a window length of 20 data points and sliding step of 20 data points without overlapping to avoid double counting for the CST signal. --EMGs: n*40*64, where n is the number of frames, 40 is the window size of the signals, and 64 is the number of HD-EMG channels. These are the input signals to the deep CNN. --Spikes_cst: n*5, where n is the number of frames, and 5 is the maximum number of spikes allowed in one frame of HD-EMG signals. These are the output signals from the deep CNN. <br> The Python implementation of the deep CNN can be found at https://github.com/ywen3/dcnn\_mu\_decomp. <br> <br>

本数据集包含高密度肌电图（High-Density Electromyography, HD-EMG）信号与对应神经驱动信号，后者以复合 spike 序列（composite spike train, CST）形式呈现，用于训练并验证用于神经驱动估计的深度卷积神经网络（deep CNN）框架。
本研究纳入5名受试者（编号uc4、uc8、uc11、uc25、uc26）的处理后数据，每名受试者对应两块肌肉：股外侧肌（vastus lateralis, VL）与股内侧肌（vastus medialis, VM），实验覆盖两个测试时段（d1与d2）。在每个测试时段中，受试者完成3次等长收缩任务，收缩强度为最大自主收缩（maximum voluntary contraction, MVC）的20%。
详细信息请参阅本团队的研究论文：https://doi.org/10.1101/2022.08.31.505855。
原始HD-EMG信号与对应CST的采样率为2048 Hz。本研究采用窗长为40个数据点、滑动步长为20个数据点且重叠量为20个数据点的窗口对HD-EMG信号进行分段；针对CST信号，则采用窗长为20个数据点、滑动步长为20个数据点的无重叠窗口，以避免重复计数。
—— 肌电信号（EMGs）：维度为n×40×64，其中n为总帧数，40为信号窗口尺寸，64为HD-EMG通道总数，该数据作为深度CNN的输入信号。
—— Spikes_cst：维度为n×5，其中n为总帧数，5为单帧HD-EMG信号允许的最大spike数量，该数据作为深度CNN的输出目标。
该深度CNN的Python实现代码可从以下链接获取：https://github.com/ywen3/dcnn_mu_decomp。