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 language||English (US)|
|Title of host publication||Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019|
|Editors||Michael B. Matthews|
|Publisher||IEEE Computer Society|
|Number of pages||4|
|State||Published - Nov 2019|
|Event||53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 - Pacific Grove, United States|
Duration: Nov 3 2019 → Nov 6 2019
|Name||Conference Record - Asilomar Conference on Signals, Systems and Computers|
|Conference||53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019|
|Period||11/3/19 → 11/6/19|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This research was funded in part by the NIH under Grant R01-MH111413-01 and in part by the internal fund at the University of Minnesota. W. Zhao is a Mistletoe Research Fellow and the receipient of the Unfettered Research Grant.
© 2019 IEEE.