On the blind source separation of human electroencephalogram by approximate joint diagonalization of second order statistics

TL;DR

This paper reviews blind source separation (BSS) methods for EEG analysis, emphasizing a new approach using approximate joint diagonalization of Fourier cospectral matrices for improved extraction of neural signals.

Contribution

It introduces a simple, efficient BSS scheme based on approximate joint diagonalization in the time-frequency domain, extending existing SOS-based methods.

Findings

AJDC method effectively separates EEG sources

Time-frequency BSS captures variations in EEG data

AJDC outperforms traditional SOS-based techniques

Abstract

Over the last ten years blind source separation (BSS) has become a prominent processing tool in the study of human electroencephalography (EEG). Without relying on head modeling BSS aims at estimating both the waveform and the scalp spatial pattern of the intracranial dipolar current responsible of the observed EEG. In this review we begin by placing the BSS linear instantaneous model of EEG within the framework of brain volume conduction theory. We then review the concept and current practice of BSS based on second-order statistics (SOS) and on higher-order statistics (HOS), the latter better known as independent component analysis (ICA). Using neurophysiological knowledge we consider the fitness of SOS-based and HOS-based methods for the extraction of spontaneous and induced EEG and their separation from extra-cranial artifacts. We then illustrate a general BSS scheme operating in the time-frequency domain using SOS only. The scheme readily extends to further data expansions in order to capture experimental source of variations as well. A simple and efficient implementation based on the approximate joint diagonalization of Fourier cospectral matrices is described (AJDC). We conclude discussing useful aspects of BSS analysis of EEG, including its assumptions and limitations.