{"ID":2829167,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.13870","arxiv_id":"2512.13870","title":"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: $2\\times2$; 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 ($\\mathrm{R}^2_\\mathrm{vw}$) across all models, with the multilayer perceptron yielding the best result ($86.68 \\pm 0.33 \\%$). 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.","short_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-...","url_abs":"https://arxiv.org/abs/2512.13870","url_pdf":"https://arxiv.org/pdf/2512.13870v4","authors":"[\"Ricardo Gonçalves Molinari\",\"Leonardo Abdala Elias\"]","published":"2025-12-15T19:58:18Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.LG\",\"eess.SY\"]","methods":"[]","has_code":false}