PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Blind Source Separation Techniques for Decomposing Evoked Brain Signals
Klaus-Robert Müller, Ricardo Vigario, Frank Meinecke and Andreas Ziehe
International Journal of Bifurcation and Chaos Volume 14, Number 2, pp. 773-791, 2004.

Abstract

Recently blind source separation (BSS) methods have been highly successful when applied to biomedical data. This paper reviews the concept of BSS and demonstrates its usefulness in the context of event-related MEG measurements. In a rst experiment we apply BSS to artifact identi cation of raw MEG data and discuss how the quality of the resulting independent component projections can be evaluated. The second part of our study considers averaged data of event-related magnetic elds. Here, it is particularly important to monitor and thus avoid possible over tting due to limited sample size. A stability assessment of the BSS decomposition allows to solve this task and an additional grouping of the BSS components reveals interesting structure, that could ultimately be used for gaining a better physiological modeling of the data.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:779
Deposited By:Klaus-Robert Müller
Deposited On:30 December 2004