{"ID":2874571,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.04088","arxiv_id":"2509.04088","title":"Spiking Neural Network Decoders of Finger Forces from High-Density Intramuscular Microelectrode Arrays","abstract":"Restoring naturalistic finger control in assistive technologies requires the continuous decoding of motor intent with high accuracy, efficiency, and robustness. Here, we present a spike-based decoding framework that integrates spiking neural networks (SNNs) with motor unit activity extracted from high-density intramuscular microelectrode arrays. We demonstrate simultaneous and proportional decoding of individual finger forces from motor unit spike trains during isometric contractions at 15% of maximum voluntary contraction using SNNs. We systematically evaluated alternative SNN decoder configurations and compared two possible input modalities: physiologically grounded motor unit spike trains and spike-encoded intramuscular EMG signals. Through this comparison, we quantified trade-offs between decoding accuracy, memory footprint, and robustness to input errors. The results showed that shallow SNNs can reliably decode finger-level motor intent with competitive accuracy and minimal latency, while operating with reduced memory requirements and without the need for external preprocessing buffers. This work provides a practical blueprint for integrating SNNs into finger-level force decoding systems, demonstrating how the choice of input representation can be strategically tailored to meet application-specific requirements for accuracy, robustness, and memory efficiency.","short_abstract":"Restoring naturalistic finger control in assistive technologies requires the continuous decoding of motor intent with high accuracy, efficiency, and robustness. Here, we present a spike-based decoding framework that integrates spiking neural networks (SNNs) with motor unit activity extracted from high-density intramusc...","url_abs":"https://arxiv.org/abs/2509.04088","url_pdf":"https://arxiv.org/pdf/2509.04088v1","authors":"[\"Farah Baracat\",\"Agnese Grison\",\"Dario Farina\",\"Giacomo Indiveri\",\"Elisa Donati\"]","published":"2025-09-04T10:43:43Z","proceeding":"cs.HC","tasks":"[\"cs.HC\",\"eess.SP\"]","methods":"[]","has_code":false}