TY - GEN
T1 - Block-Sparse Modeling for Compressed Sensing of Neural Action Potentials and Local Field Potentials
AU - Zhao, Wenfeng
AU - Wu, Tong
AU - Xu, Jian
AU - Zhao, Qi
AU - Yang, Zhi
PY - 2019/11
Y1 - 2019/11
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85083332572&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85083332572&partnerID=8YFLogxK
U2 - 10.1109/IEEECONF44664.2019.9048841
DO - 10.1109/IEEECONF44664.2019.9048841
M3 - Conference contribution
AN - SCOPUS:85083332572
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 2097
EP - 2100
BT - Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
Y2 - 3 November 2019 through 6 November 2019
ER -