Paper Info
Title
Compressive Sensing of Neural Action Potentials by Designing Overcomplete Dictionaries
Abstract
Long-term wireless neural recording systems which are subject to stringent power consumption, are highly desired to reduce the rate of data transmission and computation complexity. In this paper, we propose using a combination of on-chip neural action potentials ('spikes') detection system and compressive sensing (CS) techniques to reduce the power required for data transmission and a circulant and toeplitz matrix to reduce the computation complexity consequently further reduce the power consumption. We use the K-SVD algorithm for adapting dictionaries in order to achieve sparse signal representations of spikes and iterative shrinkage thresholding (IST) algorithm to reconstruct them. Our results show that, using the data from simultaneously recorded from brain cortex of anesthetic SD-rats, the mean compression ratio is 60:1 achieved for 13.7-dB SNDR recovery using this mechanism.
Year
DOI
Venue
2013
10.1109/GreenCom-iThings-CPSCom.2013.343
GreenCom/iThings/CPScom
Keywords
Field
DocType
iterative shrinkage thresholding algorithm,signal representation,toeplitz matrix,anesthetic sd-rats,k-svd,stringent power consumption,computation complexity rate,sndr recovery,data transmission rate,k-svd algorithm,bioelectric potentials,medical signal detection,ist,power consumption,neurophysiology,compressive sensing,adapting dictionaries,compressive sensing techniques,dictionary,prosthetics,long-term wireless neural recording system,neural signals,neural action potentials,designing overcomplete dictionaries,computational complexity,sparse signal representations,computation complexity,compressed sensing,mean compression ratio,detection system,brain,noise figure 13.7 db,signal reconstruction,microelectrodes,on-chip neural action potential spike detection system,on-chip neural action potential,data transmission,circulant matrix,brain cortex,overcomplete dictionaries,singular value decomposition,iterative methods,computation complexity reduction,power consumption reduction
Data transmission,K-SVD,Iterative method,Computer science,Algorithm,Toeplitz matrix,Artificial intelligence,Thresholding,Compressed sensing,Signal reconstruction,Machine learning,Computational complexity theory
Conference
Citations 
PageRank 
References 
0
0.34
9
Authors
8
Name
Order
Citations
PageRank
Shuai Zhou112.41
Bowei Dai210.72
Yin Xiang3102.64
Sheng-Wei Xu4147.43
bingchen zhang511017.19
Yilin Song6149.61
Mixia Wang714.10
Xinxia Cai819.85