Converging Evidence of Linear Independent Components in EEG

Blind source separation (BSS) has been proposed as a method to analyze multi-channel electroencephalography (EEG) data. A basic issue in applying BSS algorithms is the validity of the independence assumption. In this paper we investigate whether EEG can be considered to be a linear combination of independent sources. Linear BSS can be obtained under the assumptions of non-Gaussian, non-stationary, or non-white independent sources. If the linear independence hypothesis is violated these three different conditions will not necessarily lead to the same result. We show, using 64 channel EEG data, that different algorithms which incorporate the three different assumptions lead to the same results, thus supporting the linear independence hypothesis. keywords: Blind source separation (BSS), electroencephalography (EEG), non-Gaussian, non-white, nonstationary.