Converging Evidence of Linear Independent Components in EEG

Converging Evidence of Linear Independent Components in EEG

Abstract—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.