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>