Multi-channel EEG compression based on matrix and tensor decompositions

Author

Dauwels, Justin ; Srinivasan, K. ; Ramasubba Reddy, M. ; Cichocki, Andrzej

Author_Institution

Nanyang Technol. Univ., Singapore, Singapore

fYear

2011

fDate

22-27 May 2011

Firstpage

629

Lastpage

632

Abstract

Compression schemes for EEG signals are developed based on matrix and tensor decomposition. Various ways to arrange EEG signals into matrices and tensors are explored, and several matrix and tensor decomposition schemes are applied, including SVD, CUR, PARAFAC, the Tucker decomposition, and recent random fiber selection approaches. Rate-distortion curves for the proposed matrix and tensor-based EEG compression schemes are computed. It shown that PARAFAC has the best compression performance in this context.

Keywords

data compression; electroencephalography; matrix decomposition; medical signal processing; rate distortion theory; tensors; CUR decomposition; EEG signal compression scheme; PARAFAC decomposition; SVD; Tucker decomposition; matrix decomposition scheme; multichannel EEG compression; random fiber selection approaches; rate-distortion curves; tensor decomposition sheme; Brain modeling; Compressed sensing; Discrete wavelet transforms; Electroencephalography; Matrix decomposition; Tensile stress; EEG; PARAFAC; SVD; Tucker; tensor decomposition;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on

Conference_Location

Prague

ISSN

1520-6149

Print_ISBN

978-1-4577-0538-0

Electronic_ISBN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2011.5946482

Filename

5946482