Intramuscular microelectrode arrays enable highly-accurate neural decoding of hand movements
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915494512033792 |
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| author | Grison, Agnese Pereda, Jaime Ibanez Muceli, Silvia Kundu, Aritra Baracat, Farah Indiveri, Giacomo Donati, Elisa Farina, Dario |
| author_facet | Grison, Agnese Pereda, Jaime Ibanez Muceli, Silvia Kundu, Aritra Baracat, Farah Indiveri, Giacomo Donati, Elisa Farina, Dario |
| contents | Decoding the activity of the nervous system is a critical challenge in neuroscience and neural interfacing. In this study, we present a neuromuscular recording system that enables large-scale sampling of muscle activity using microelectrode arrays with over 100 channels embedded in forearm muscles. These arrays captured intramuscular high-density signals that were decoded into patterns of activation of spinal motoneurons. In two healthy participants, we recorded high-density intramuscular activity during single- and multi-digit contractions, revealing distinct motoneuron recruitment patterns specific to each task. Based on these patterns, we achieved perfect classification accuracy (100%) for 12 single- and multi-digit tasks and over 96% accuracy for up to 16 tasks, significantly outperforming state-of-the-art EMG classification methods. This intramuscular high-density system and classification method represent an advancement in neural interfacing, with the potential to improve human-computer interaction and the control of assistive technologies, particularly for replacing or restoring impaired motor function. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_11016 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Intramuscular microelectrode arrays enable highly-accurate neural decoding of hand movements Grison, Agnese Pereda, Jaime Ibanez Muceli, Silvia Kundu, Aritra Baracat, Farah Indiveri, Giacomo Donati, Elisa Farina, Dario Neurons and Cognition Human-Computer Interaction Robotics Signal Processing Decoding the activity of the nervous system is a critical challenge in neuroscience and neural interfacing. In this study, we present a neuromuscular recording system that enables large-scale sampling of muscle activity using microelectrode arrays with over 100 channels embedded in forearm muscles. These arrays captured intramuscular high-density signals that were decoded into patterns of activation of spinal motoneurons. In two healthy participants, we recorded high-density intramuscular activity during single- and multi-digit contractions, revealing distinct motoneuron recruitment patterns specific to each task. Based on these patterns, we achieved perfect classification accuracy (100%) for 12 single- and multi-digit tasks and over 96% accuracy for up to 16 tasks, significantly outperforming state-of-the-art EMG classification methods. This intramuscular high-density system and classification method represent an advancement in neural interfacing, with the potential to improve human-computer interaction and the control of assistive technologies, particularly for replacing or restoring impaired motor function. |
| title | Intramuscular microelectrode arrays enable highly-accurate neural decoding of hand movements |
| topic | Neurons and Cognition Human-Computer Interaction Robotics Signal Processing |
| url | https://arxiv.org/abs/2410.11016 |