Block-Sparse Modeling for Compressed Sensing of Neural Action Potentials and Local Field Potentials

Wenfeng Zhao, Tong Wu, Jian Xu, Qi Zhao, Zhi Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents our attempt for the efficient sparse modeling and recovery for Compressed Sensing (CS) of extracellular neural action and local field potentials (APs LFPs). Both type of neural signals can be modeled as block-sparse in DCT (Discrete-Cosine Transform) domain, where we exploit the spectral information to determine the block boundaries, including bandpass filter pole information used for spike detection and the corner frequency of local filed potentials, respectively. Binary-Weighted ℓ1-minimization (BW-ℓ1-min) is proposed for neural signal recovery with their respective block boundary information. Experimental results demonstrate that block-sparse modeling and BW-ℓ1-min recovery lead to more than 5-dB signal-to-noise ratio improvement for both AP and LFP signals as compared to the standard ℓ1-minimization algorithm.

Original languageEnglish (US)
Title of host publicationConference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages2097-2100
Number of pages4
ISBN (Electronic)9781728143002
DOIs
StatePublished - Nov 2019
Event53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 - Pacific Grove, United States
Duration: Nov 3 2019Nov 6 2019

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2019-November
ISSN (Print)1058-6393

Conference

Conference53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
CountryUnited States
CityPacific Grove
Period11/3/1911/6/19

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