Evaluating Convolution Neural Network Architecture for Neural Drive Decoding from High-Density Surface Electromyography

Prior studies demonstrated encouraging results in the application of convolution neural network models (CNN), one-dimensional (1D CNN), or three-dimensional (3D CNN) convolutional layers to decode the neural drive to muscles from highdensity surface electromyography (HD-sEMG) signals. However, the impact of the dimensionality (1D or 3D) of the convolutional layers on the performance of the deep CNN models using the same dataset has yet to be investigated. This study assesses the performance of 3D CNNs and 1D CNNs in extracting the neural drive as a cumulative spike train (CST) under various window sizes and step sizes that are critical parameters in decoding neural drives. Experimental HD-sEMG dataset sourced from the gastrocnemius medialis muscle of three participants, alongside the corresponding neural drive decoded using the convolution kernel compensation (CKC) algorithm, was employed to train and validate the 1D and 3D CNN models. We compared the F1 score and correlation coefficient between the CST from CKC and those from both 1D and 3D CNN models, revealing that 1D CNN performs more effectively with larger sliding window sizes (80 or 120 samples) with a peak F1 score of 0.84 and a correlation of 0.94. In contrast, 3D CNN achieves peak F1 score (0.83) and correlation (0.92) with smaller sliding window sizes (20 or 40 samples), indicating reduced latency in using 3D CNN to decode neural drives. Both models experience a performance decline as the step size increases. Furthermore, this research evaluates the computational cost of 1D and 3D CNN models, finding that the 3D CNN model requires significantly more computational resources (938G FLOPs) than the 1D CNN model (60G FLOPs). The results elucidate significant distinctions between CNN architectures and identify optimal parameters and model selection for precise and real-time neural drive decoding.