发表机构
National University of Singapore（新加坡国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出神经运动层级网络（NHN），通过生理启发的层级结构学习潜在神经运动状态，在sEMG解码中实现参数高效的稳健泛化，显著降低误差并减少参数。
AI 中文摘要
表面肌电（sEMG）提供了一种可穿戴、非侵入性的神经肌肉活动接口，用于运动解码和人机交互。由于sEMG与神经肌肉活动之间的关系在不同用户和会话之间存在差异，而任务相关的动态跨越通道和多个时间尺度，群体规模解码仍然困难。从任务标签学习波形到输出的映射，将记录变异性与协调运动活动之间的区分隐式化。我们引入了神经运动层级网络（NHN），该网络从任务监督中学习一个紧凑的潜在神经运动状态，以表示任务相关的神经肌肉协调。NHN通过受神经运动控制启发的层级结构构建此潜在状态，该结构适应记录统计量，同时保留相对信息。其时空编码器使用参数高效的通道交互，并用多时间尺度历史调制特征。所得特征产生学习到的运动原语的候选激活，这些激活被时间积分并连续加权以形成状态。理论分析表征了NHN核心机制的效率、时间行为和优化。我们评估了该架构在emg2pose上的连续手部姿态估计和emg2qwerty上的触摸打字识别。在emg2pose上，相对于Hadidi等人最佳任务特定变体，NHN在所有三个泛化分割的回归和跟踪中，将用户平均角度误差降低了0.52%至2.84%，同时使用减少了48.42%至48.51%的参数。在emg2qwerty上，相对于SplashNet-Upscale，NHN在零样本下将束搜索字符错误率降低了19.40%，微调后降低了30.42%，同时使用减少了65.86%的参数。生理引导的潜在神经运动状态推断支持参数高效的sEMG解码。
英文摘要
Surface electromyography (sEMG) provides a wearable, noninvasive interface to neuromuscular activity for movement decoding and human-computer interaction. Population-scale decoding remains difficult because the relationship between sEMG and neuromuscular activity varies across users and sessions, while task-relevant dynamics span channels and multiple timescales. Learning waveform-to-output mappings from task labels leaves the distinction between recording variability and coordinated motor activity implicit. We introduce the Neuromotor Hierarchy Network (NHN), which learns a compact latent neuromotor state from task supervision to represent task-relevant neuromuscular coordination. NHN constructs this latent state through a hierarchy inspired by neuromotor organization. It adapts recording statistics while preserving relative intensity. Its spatiotemporal encoder uses parameter-efficient channel interactions and modulates features with multi-timescale history. The resulting features yield candidate activations of learned motor primitives, which are temporally integrated and continuously weighted to form the state. Theoretical analysis characterizes the efficiency, temporal behavior, and optimization of NHN's core mechanisms. We evaluate the architecture for both continuous hand-pose estimation on emg2pose and touch-typing recognition on emg2qwerty. On emg2pose, NHN reduces user-averaged angular error by 0.52% to 2.84% across all three generalization splits in both Regression and Tracking relative to Hadidi et al.'s best task-specific variants, using 48.42% to 48.51% fewer parameters. On emg2qwerty, NHN reduces beam-search character error rate by 19.40% zero-shot and 30.42% after fine-tuning relative to SplashNet-Upscale, using 65.86% fewer parameters. Physiology-guided inference of a latent neuromotor state supports parameter-efficient sEMG decoding.
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