Do Spatial Descriptors Improve Multi-DoF Finger Movement Decoding from HD sEMG?

Abstract

Restoring hand function requires simultaneous and proportional control (SPC) of multiple degrees of freedom (DoFs). This study evaluated the multichannel linear descriptors-based block field method (MLD-BFM) against conventional feature extraction approaches for continuous decoding of five finger-joint DoFs using high-density surface electromyography (HD sEMG). Twenty-one healthy participants performed dynamic sinusoidal finger movements while HD sEMG signals were recorded from the proximal forearm. MLD-BFM extracted spatial descriptors including effective field strength (Σ), field-strength variation rate (Φ), and spatial complexity (Ω). Performance was optimized (block size: 22; window: 0.15,s) and compared with conventional time-domain features, root mean square (RMS) and mean absolute value plus waveform length (MAV-WL), as well as dimensionality reduction methods (PCA and NMF), using multi-output regression models. MLD-BFM achieved the highest mean variance-weighted coefficient of determination (R^2_vw) across all models, with the multilayer perceptron yielding the best result (86.68 0.33 \%). However, the improvement was not statistically significant relative to time-domain features, suggesting that dense multichannel recordings already encode spatial information through amplitude-based descriptors. MLD-BFM significantly outperformed dimensionality reduction approaches, indicating that preserving the spatial resolution of HD sEMG is critical for accurate multi-DoF finger movement regression.

Reproductions