DocumentCode
1672151
Title
Compressive multichannel cortical signal recording
Author
Hosseini Kamal, Mahdad ; Shoaran, Mahsa ; Leblebici, Yusuf ; Schmid, A. ; Vandergheynst, P.
Author_Institution
Ecole Polytech. Fed. de Lausanne (EPFL), Lausanne, Switzerland
fYear
2013
Firstpage
4305
Lastpage
4309
Abstract
This paper presents a novel approach to acquire multichannel wireless intracranial neural data based on a compressive sensing scheme. The designed circuits are extremely compact and low-power which confirms the relevance of the proposed approach for multichannel high-density neural interfaces. The proposed compression model enables the acquisition system to record from a large number of channels by reducing the transmission power per channel. Our main contributions are the twofold. First, a CMOS compressive sensing system to realize multichannel intracranial neural recording is described. Second, we explain a joint sparse decoding algorithm to recover the multichannel neural data. The idea has been implemented at system as well as circuit levels. The simulation results reveal that the multichannel intracranial neural data can be acquired by compression ratios as high as four.
Keywords
compressed sensing; electroencephalography; medical signal processing; signal reconstruction; CMOS compressive sensing system; acquisition system; compact circuits; compressive multichannel cortical signal recording; compressive sensing scheme; joint sparse decoding algorithm; low power circuits; multichannel high density neural interfaces; multichannel wireless intracranial neural data; transmission power per channel; Arrays; Brain modeling; Compressed sensing; Electroencephalography; Q measurement; Signal to noise ratio; Vectors; Compressive Sensing; Multichannel Signal Processing; Sparsity; iEEG;
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
Conference_Location
Vancouver, BC
ISSN
1520-6149
Type
conf
DOI
10.1109/ICASSP.2013.6638472
Filename
6638472
Link To Document