  Compressive sensing (CS) is a signal processing technique that enables
sub-Nyquist sampling and near lossless reconstruction of a sparse signal. The
technique is particularly appealing for neural signal processing since it
avoids the issues relevant to high sampling rate and large data storage. In
this project, different CS reconstruction algorithms were tested on raw action
potential signals recorded in our lab. Two numerical criteria were set to
evaluate the performance of different CS algorithms: Compression Ratio (CR) and
Signal-to-Noise Ratio (SNR). In order to do this, individual CS algorithm
testing platforms for the EEG data were constructed within MATLAB scheme. The
main considerations for the project were the following. 1) Feasibility of the
dictionary 2) Tolerance to non-sparsity 3) Applicability of thresholding or
interpolation.
