EEG montage analysis in the Blind Source Separation framework
Résumé
Blind Source Separation (BSS) is a relatively recent technique, more and more applied in electroen- cephalographic (EEG)signal processing. Still, the classical mixing model of the BSS does not take into account there al recording set-up. In fact, a major problem in electrophysiological recording systems (e.g. ECG, EEG,EMG) is to find a region in the human body whose bio-potential activity can be considered as neutral as possible i.e.,a quasi-inactive reference place. Nowadays, it iswel lknown that it is impossible to find a “zero-potential” site on the humanbody .In particular, the most common way of performing EEG recordings is by using as a common reference an electrode placed some where on the head. Starting from this Common Reference Montage (CRM), several other montages can be constructed to obtain alter-native interpretation or processings olutions. Regardless of the chosen montage, the reference electrode intervenes in the mixing model of the BSS.The objective of this work is to analyse the influence of the montage on the mixing matrix and the quality of the BSS solution.This paper proposes to formalize the source separation problem in a nonzero-potential reference context and shows that the Average Reference Montage (ARM), augmented by avirtual “average measure”, leads to better source separation results (separability index IS). This conclusion is supported by simulated EEGs using the most common montages i.e., Common Reference Montage, Average Reference Montage and Bipolar-Longitudinal Montage, aswell as by real EEG examples.