DocumentCode :
429189
Title :
Stepwise model order estimation in blind source separation applied to ictal EEG
Author :
Hesse, C.W. ; James, C.J.
Author_Institution :
Inst. of Sound & Vibration Res., Southampton Univ., UK
Volume :
1
fYear :
2004
fDate :
1-5 Sept. 2004
Firstpage :
986
Lastpage :
989
Abstract :
Most algorithms for blind source separation (BSS) or independent component analysis (ICA) assume an equal number of sources as sensors. For multichannel electrophysiological recordings, such as the electroencephalogram (EEG), however, there are often far fewer sources of neurophysiologically relevant activity than the number of sensors. This adds a model order estimation problem to the source separation problem. Conventional estimates of the number of sources are based on the dominant eigenvalues of the data covariance matrix, obtained from principal component analysis (PCA), whose corresponding eigenvectors are also used for prewhitening. It is well known that PCA is susceptible to noise, leading to incorrect model order estimates and data distortion, which in turn limit the accuracy of the source estimates. It is therefore highly desirable to determine the correct number of sources and their spatial topographies directly, without PCA-based data truncation or prewhitening. In this work, we present a stepwise BSS method for extracting only the sources necessary for a sufficiently good least-square fit to the data. This simultaneously yields model order and source estimates, which we examine at different noise levels. We also show how only a few neurophysiologically meaningful components can be extracted from 25-channel ictal EEG.
Keywords :
blind source separation; electroencephalography; independent component analysis; least squares approximations; medical signal processing; neurophysiology; blind source separation; electroencephalogram; ictal EEG; independent component analysis; least-square fit; multichannel electrophysiological recording; neurophysiologically relevant activity; stepwise model order estimation; Blind source separation; Brain modeling; Covariance matrix; Data mining; Eigenvalues and eigenfunctions; Electroencephalography; Electrophysiology; Independent component analysis; Principal component analysis; Source separation; Blind Source Separation; EEG; Independent Component Analysis; Model Order Estimation; Stepwise BSS/ICA;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-8439-3
Type :
conf
DOI :
10.1109/IEMBS.2004.1403327
Filename :
1403327
Link To Document :
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