EEG montage analysis in the Blind Source Separation framework

Abstract Blind Source Separation (BSS) is a relatively recent technique, more and more applied in electroencephalographic (EEG) signal processing. Still, the classical mixing model of the BSS does not take into account the real 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 is well known that it is impossible to find a “zero-potential” site on the human body. In particular, the most common way of performing EEG recordings is by using as a common reference an electrode placed somewhere on the head. Starting from this Common Reference Montage (CRM), several other montages can be constructed to obtain alternative interpretation or processing solutions. 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 non zero-potential reference context and shows that the Average Reference Montage (ARM), augmented by a virtual “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, as well as by real EEG examples.