Temporal convolutional network architectures: a novel simultaneous spatio-temporal model for comparative analysis

Conventional temporal-based deep learning models often fail to extract inter- channel information from electromyographic (EMG) signals. Existing spatio-temporal approaches typically sequentially combine spatial and temporal networks, but this strategy increases model complexity and parameter count. We introduce a simultaneous spatio-temporal convolutional deep network, which integrates spatial and temporal feature extraction connections within a single, explainable deep network. To evaluate the new architecture through a comprehensive comparative analysis, we compared its performance and model size with three other established decoding methods. We used two internal and two publicly available EMG databases. We report that the application of convolutional filters in both spatial and temporal directions simultaneously enhances myoelectric decoding accuracy. Finally, we explain the proposed model using the saliency maps method. The findings indicate that the proposed simultaneous spatio-temporal configuration offers reliable classification performance and is well-suited for real-time on-board deployment. The proposed model explains how simultaneous spatio-temporal convolution enhances the contribution of both temporal and spatial components of EMG activity, resulting in improved classification performance.