Model Order Estimation for Independent Component Analysis of Epoched EEG Signals

Peter Mondrup Rasmussen, Morten Mørup, Lars Kai Hansen, Sidse Marie Arnfred

AbstractIn analysis of multi-channel event related EEG signals indepedent component analysis (ICA) has become a widely used tool to attempt to separate the data into neural activity, physiological and non-physiological artifacts. High density elctrode systems offer an opportunity to estimate a corresponding large number of independent components (ICs). However, too large a number of ICs leads to overfitting of the ICA model, which can have a major impact on the model validity. Consequently, finding the optimal number of components
in the ICA model is an important problem. In this paper we present a method for model order selection, based on a probabilistic framework. The proposed method is a modification of the Molgedey Schuster (MS) algorithm to epoched, i.e. event related data. Thus, the contribution of the present paper can be summarized as follows: 1) We advocate MS as a low complexity ICA alternative for EEG. 2) We define an epoch based likelihood function for estimation of a principled unbiased ’test error’. 3) Based on the unbiased test error measure we perform model order selection for ICA of EEG. Applied to a 64 channel EEG data set we were able to determine an optimum order of the ICA model and to extract 22 ICs related to the neurophysiological stimulus responses as well as ICs related to physiological- and non-physiological noise. Furthermore, highly relevant high frequency response information was captured by the ICA model.
KeywordsEEG; Event related potentials; Independent component analysis (ICA); Molgedey Schuster; TDSEP;Model Selection, Cross validation.
TypeConference paper [With referee]
ConferenceBiosignals 2008 International Conference on Bio-inspired Systems and Signal Processing.
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing

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