Noninvasive Neural Interfacing With Wearable Muscle Sensors: Combining Convolutive Blind Source Separation Methods and Deep Learning Techniques for Neural Decoding

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.

Automated summary

Neural interfacing is crucial for understanding movement neurophysiology and developing human-machine interaction systems. Recent advances in analyzing surface electromyographic signals provide a new way to establish human interfaces by reverse engineering neural information from skeletal muscles' electrical activity. Breakthroughs in convolutive blind source separation methods and deep learning techniques offer new possibilities for signal processing in neural interfacing.