Determining the number of sources in high-density EEG recordings of event-related potentials by model order selection

Abstract

To high-density electroencephalography (EEG) recordings, determining the number of sources to separate the signal and the noise subspace is very important. A mostly used criterion is that percentage of variance of raw data explained by the selected principal components composing the signal space should be over 90%. Recently, a model order selection method named as GAP has been proposed. We investigated the two methods by performing independent component analysis (ICA) on the estimated signal subspace, assuming the number of selected principal components composing the signal subspace is equal to the number of sources of brain activities. Through examining wavelet-filtered EEG recordings (128 electrodes) of ERPs, ICA with the reference to GAP decomposed 14 selected principal components reliably into 14 independent components, and ICA decomposition with the variance explained method was not reliable, indicating that the number of sources, as well as the signal subspace, should be well estimated through GAP.