A Comparison of Time Structure and Statistically Based BSS Methods in the Context of Long-Term Epileptiform EEG Recordings

Abstract

Blind source separation (BSS) techniques are increasingly being applied to the analysis of biomedical signals in general and electroencephalographic (EEG) signals in particular. The analysis of the long-term monitored epileptiform EEG presents characteristic problems for the implementation of BSS techniques because the ongoing EEG has time varying frequency content which can be both slowly varying and yet also include short bursts of neurophysiologically meaningful activity. Since statistically based BSS methods rely on sample- estimates, which generally require larger window sizes, these methods may extract neurophysiologically uninformative components over short data segments. Here we show that BSS techniques using signal time structure succeed in extracting neurophysiologically meaningful components where their statistical counterparts fail. To this end we use an algorithm that extracts linear mixtures of nonstationary sources, without pre- whitening, through joint diagonalisation of a number of windowed, lagged cross-covariance matrices. We show that this is extremely useful in tracking seizure onset in the epileptiform EEG.