Compressed Sensing of Extracellular Neurophysiology Signals: A Review - Sun, Biao; Zhao, Wenfeng (Frontiers in Neuroscience, 2021)
This article presents a comprehensive review of literature on the Compressed Sensing of neurophysiology signals. Compressed Sensing (CS) is a promising technique to achieve high-fidelity, low-rate, and hardware-efficient neural signal compression tasks for wireless streaming of massively parallel neural recording channels in next-generation neural interface technologies. The main objective is to provide a timely retrospective on applying CS theory to the extracellular brain signals in the past decade. We will present a comprehensive review on CS-based neural recording system architecture, CS encoder hardware exploration and implementation, sparse representation of neural signals, and signal reconstruction algorithms. Deep Learning-based methods are also discussed and compared with traditional CS-based approaches. We will also extend our discussion to cover the technical challenges and prospects in this emerging field.