Neural interfacing is essential for advancing our fundamental understanding of movement neurophysiology and for developing human-machine interaction systems. This can be achieved at different levels of the central nervous system (CNS) and peripheral nervous system (PNS); however, direct neural interfaces with brain and nerve tissues face important challenges and are currently limited to clinical cases of severe motor impairment. Recent advances in electronics and signal processing for recording and analyzing surface electromyographic (sEMG) signals allow for a radically new way of establishing human interfaces by reverse engineering the neural information embedded in the electrical activity of skeletal muscles. This approach provides a window into the spiking activity of motor neurons in the spinal cord. In this article, we present a brief overview of neural interfaces and discuss the properties of multichannel sEMG in comparison to other CNS and PNS recording modalities. We then describe signal processing approaches for neural interfacing from sEMG, with a focus on recent breakthroughs in convolutive blind source separation (BSS) methods and deep learning techniques. When combined, these approaches establish unique noninvasive human-machine interfaces for neurotechnologies, with applications in medical devices and large-scale consumer electronics.

中文翻译：

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与可穿戴肌肉传感器的无创神经接口：结合卷积盲源分离方法和深度学习技术进行神经解码


神经接口对于增进我们对运动神经生理学的基本理解和开发人机交互系统至关重要。这可以在中枢神经系统（CNS）和周围神经系统（PNS）的不同水平上实现；然而，与大脑和神经组织的直接神经接口面临着重大挑战，目前仅限于严重运动障碍的临床病例。用于记录和分析表面肌电 (sEMG) 信号的电子学和信号处理领域的最新进展，通过对嵌入骨骼肌电活动中的神经信息进行逆向工程，提供了一种建立人机界面的全新方法。这种方法为了解脊髓中运动神经元的尖峰活动提供了一个窗口。在本文中，我们简要概述了神经接口，并讨论了多通道 sEMG 与其他 CNS 和 PNS 记录方式相比的特性。然后，我们描述了 sEMG 神经接口的信号处理方法，重点关注卷积盲源分离（BSS）方法和深度学习技术的最新突破。这些方法结合起来，为神经技术建立了独特的非侵入性人机界面，并应用于医疗设备和大型消费电子产品。