Measurement of neural signal provides important value for study of brain function and the pathogenesis of neurological. With emerging extensive research of electrical activity, more and more neural signal need to be collected, transmitted and stored, making the compression processing of neural signal become important part of digital signal processing. In recent years, ASIC-based wireless neural signal acquisition system has been developed rapidly, encountered strict restrictions on power consumption which is dominant determined by the data rate and complexity of algorithm. In order to reduce power consumption, lower data rate and algorithm with lower complexity needed to be selected when design a neural acquisition system. This paper focus on neural signal compression method based on compressed sensing and its performance and compare it with conventional compression algorithm. We compare complexity of various algorithms in the view of circuit complement, show that the complexity of neural signal compression can be dramatically reduced by using specially designed compressed sensing matrix, thereby reducing the system power consumption.
A Fully Integrated Wireless Compressed Sensing Neural Signal Acquisition System for Chronic Recording and Brain Machine Interface
