Spiking Neural Network Decoders of Finger Forces from High-Density Intramuscular Microelectrode Arrays
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918135749148672 |
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| author | Baracat, Farah Grison, Agnese Farina, Dario Indiveri, Giacomo Donati, Elisa |
| author_facet | Baracat, Farah Grison, Agnese Farina, Dario Indiveri, Giacomo Donati, Elisa |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_04088 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Spiking Neural Network Decoders of Finger Forces from High-Density Intramuscular Microelectrode Arrays Baracat, Farah Grison, Agnese Farina, Dario Indiveri, Giacomo Donati, Elisa Human-Computer Interaction Signal Processing 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. |
| title | Spiking Neural Network Decoders of Finger Forces from High-Density Intramuscular Microelectrode Arrays |
| topic | Human-Computer Interaction Signal Processing |
| url | https://arxiv.org/abs/2509.04088 |