Sensitivity of Non-Invasive Motor-Unit-Based Gesture Recognition to Signal Degradation

The information encoded by motor units has been successfully harnessed to establish high-fidelity human-machine interfaces (HMI). However, the sensitivity of these interfaces using high-density surface electromyography decomposition, a prevalent method for observing motor unit behaviour, to signal degradations commonly encountered in practical settings remains unexplored. Here, we investigated the effects of additive white Gaussian noise (WGN), channel loss, and electrode shift on pseudo-real-time MU-driven motion classification. Across six wrist movements by 13 participants, we evaluated the performance of two classifiers: linear discriminant analysis (LDA) and deep neural networks (DNN), under different noise conditions. The results indicate that spatial perturbations, including channel loss and electrode shift, significantly affected classification accuracy, with LDA being more susceptible than DNN. Conversely, under intense signal noise (WGN with 5 dB SNR), LDA outperformed DNN, and its simplicity potentially provides greater robustness in a challenging environment. Hence, application-specific signal processing considerations are required depending on the target HMI application.