Prediction of movement intentions from electromyographic (EMG) signals is typically performed with a pattern recognition approach, wherein a short dataframe of raw EMG is compressed into an instantaneous feature-encoding that is meaningful for classification. However, EMG signals are time-varying, implying that a frame-wise approach may not sufficiently incorporate temporal context into predictions, leading to erratic and unstable prediction behavior. Objective: We demonstrate that sequential prediction models and, specifically, temporal convolutional networks are able to leverage useful temporal information from EMG to achieve superior predictive performance. Methods: We compare this approach to other sequential and frame-wise models predicting 3 simultaneous hand and wrist degrees-of-freedom from 2 amputee and 13 non-amputee human subjects in a minimally constrained experiment. We also compare these models on the publicly available Ninapro and CapgMyo amputee and non-amputee datasets. Results: Temporal convolutional networks yield predictions that are more accurate and stable $(p < 0.001)$ than frame-wise models, especially during inter-class transitions, with an average response delay of 4.6 ms $(p < 0.001)$ and simpler feature-encoding. Their performance can be further improved with adaptive reinforcement training. Significance: Sequential models that incorporate temporal information from EMG achieve superior movement prediction performance and these models allow for novel types of interactive training. Conclusions: Addressing EMG decoding as a sequential modeling problem will lead to enhancements in the reliability, responsiveness, and movement complexity available from prosthesis control systems.

中文翻译：

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具有时间卷积网络的稳定响应 EMG 序列预测和自适应强化

从肌电图 (EMG) 信号预测运动意图通常使用模式识别方法执行，其中原始 EMG 的短数据帧被压缩为对分类有意义的瞬时特征编码。然而，EMG 信号是随时间变化的，这意味着逐帧方法可能无法将时间上下文充分整合到预测中，从而导致不稳定和不稳定的预测行为。客观的： 我们证明了顺序预测模型，特别是时间卷积网络能够利用来自 EMG 的有用时间信息来实现卓越的预测性能。 方法：我们将此方法与其他顺序和逐帧模型进行比较，该模型在最小约束实验中预测 2 名截肢者和 13 名非截肢者人类受试者的 3 个同时手和手腕的自由度。我们还在公开可用的 Ninapro 和 CapgMyo 截肢者和非截肢者数据集上比较了这些模型。结果： 时间卷积网络产生更准确和稳定的预测 $(p < 0.001)$ 比逐帧模型，尤其是在类间转换期间，平均响应延迟为 4.6 毫秒 $(p < 0.001)$和更简单的特征编码。他们的表现可以通过适应性强化训练进一步提高。意义： 结合来自 EMG 的时间信息的序列模型实现了卓越的运动预测性能，这些模型允许进行新型交互训练。 结论： 将 EMG 解码作为一个顺序建模问题将导致假肢控制系统的可靠性、响应性和运动复杂性的增强。