TY - GEN
T1 - Model order estimation for independent component analysis of epoched EEG signals
AU - Rasmussen, Peter Mondrup
AU - Mørup, Morten
AU - Hansen, Lars Kai
AU - Arnfred, Sidse M.
PY - 2008
Y1 - 2008
N2 - In 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 over-fitting 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.
AB - In 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 over-fitting 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.
KW - Cross validation
KW - EEG
KW - Event related potentials
KW - Independent component analysis (ICA)
KW - Model selection
KW - Molgedey schuster
KW - TDSEP
UR - http://www.scopus.com/inward/record.url?scp=55649118221&partnerID=8YFLogxK
M3 - Article in proceedings
AN - SCOPUS:55649118221
SN - 9789898111180
T3 - BIOSIGNALS 2008 - Proceedings of the 1st International Conference on Bio-inspired Systems and Signal Processing
SP - 3
EP - 10
BT - BIOSIGNALS 2008 - Proceedings of the 1st International Conference on Bio-inspired Systems and Signal Processing
T2 - BIOSIGNALS 2008 - 1st International Conference on Bio-inspired Systems and Signal Processing
Y2 - 28 January 2008 through 31 January 2008
ER -