用100+通道肌内电极阵列实现手部动作高精度解码
Intramuscular microelectrode arrays enable highly-accurate neural decoding of hand movements
- 在肌肉内植入百通道微电极阵列采集高密度神经信号
- 对12种手部动作实现100%分类准确率，16种任务超96%
- 适合假肢控制、运动功能修复等神经接口应用
神经解码是神经科学与神经接口的关键挑战。本研究提出一种神经肌肉记录系统，通过嵌入前臂肌肉的超过100个通道的微电极阵列，实现大范围肌肉活动采样。这些阵列捕捉到肌内高密度信号，并解码为脊髓运动神经元的激活模式。在两名健康受试者中，我们记录了单指与多指收缩时的高密度肌内活动，揭示了每项任务特有的运动神经元募集模式。基于这些模式，对12种单指及多指任务实现了100%的分类准确率，对最多16种任务的准确率超过96%，显著优于现有肌电（EMG）分类方法。该肌内高密度系统与分类方法代表了神经接口的重要进展，有望提升人机交互性能，尤其在替代或恢复受损运动功能方面具有潜力。
原文摘要 · Abstract (English)
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.
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