A digital multichannel neural signal processing system using compressed sensing

Abstract

This paper concerns a wireless multichannel neural recording system using a compressed sensing technique to compress the recorded data. We put forth a single and a multichannel system applying a Minimum Euclidean or Manhattan Distance Cluster-based (MDC) deterministic compressed sensing matrix. The single-channel signal processing system is composed of spike detection and data compression blocks. For the construction of the MDC matrix, the distance σ is an important parameter, which can take a value of 4 or 5. In addition, the sharing strategy is used to construct a multichannel system, and we analyze the influence of the number of the channels; scan rate on the reconstruction error, compression rate and power consumption; the influence of the signal-to-noise ratio; and reconstruction performance on neural signals. Based on the results, a 256-channel digital signal processing system, implemented in a 130-nm CMOS process, is proposed. This system has power consumption per channel of 12.5 μW and silicon area per channel of 0.03 mm2, and provides data reduction of around 90% while enabling accurate reconstruction of the original signals.