用脉冲神经网络解码肌电信号，为类脑计算提供新思路。
Assessing Neuromorphic Computing for Fingertip Force Decoding from Electromyography
- 对比脉冲神经网络与时序卷积网络解码指尖力
- 时序卷积网络误差4.44% MVC，脉冲网络8.25% MVC
- 脉冲网络具类脑潜力，优化后可逼近主流模型
高密度表面肌电（HD-sEMG）为辅助与康复控制提供了非侵入性神经接口，但将神经活动映射到用户运动意图仍具挑战。本文评估了脉冲神经网络（SNN）作为类脑架构，在解码由HD-sEMG提取的运动单元（MU）放电信号所对应的指尖力方面，与时间卷积网络（TCN）的表现对比。数据来自单名参与者（10次试验），使用双前臂电极阵列采集；通过FastICA分解获得运动单元活动，模型在重叠窗口上采用端到端因果卷积训练。在保留测试集上，TCN达到4.44% MVC RMSE（皮尔逊相关系数r = 0.974），而SNN为8.25% MVC（r = 0.922）。尽管TCN更准确，但SNN仍可视为一个现实的类脑计算基线，经适度架构与超参数优化后有望显著缩小差距。
原文摘要 · Abstract (English)
High-density surface electromyography (HD-sEMG) provides a noninvasive neural interface for assistive and rehabilitation control, but mapping neural activity to user motor intent remains challenging. We assess a spiking neural network (SNN) as a neuromorphic architecture against a temporal convolutional network (TCN) for decoding fingertip force from motor-unit (MU) firing derived from HD-sEMG. Data were collected from a single participant (10 trials) with two forearm electrode arrays; MU activity was obtained via FastICA-based decomposition, and models were trained on overlapping windows with end-to-end causal convolutions. On held-out trials, the TCN achieved 4.44% MVC RMSE (Pearson r = 0.974) while the SNN achieved 8.25% MVC (r = 0.922). While the TCN was more accurate, we view the SNN as a realistic neuromorphic baseline that could close much of this gap with modest architectural and hyperparameter refinements.
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