2021/01/30 by Hyunseok Park, Xilin Liu, Park, Hyunseok +1
Engineering · #Algorithm #Analog and Mixed-Signal Circuit Design #Artificial intelligence #Compressed sensing #Compression ratio #Computer hardware #Computer science #Computer vision #Data compression #Digital signal processing #Electrical and Bioimpedance Tomography #Engineering #FOS: Electrical engineering #Filter (signal processing) #Interpolation (computer graphics) #Lossless compression #MATLAB #Noise (video) #Nyquist rate #SIGNAL (programming language) #Sampling (signal processing) #Signal Processing (eess.SP) #Signal processing #Signal reconstruction #Sparse and Compressive Sensing Techniques #Thresholding #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2102.00284
published in arXiv (Cornell University) (Cornell University)
arxiv created 2021/01/30 · openalex publication_date 2021/01/30 · arxiv updated 2021/02/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.