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8
An Introduction to EEG Source
Analysis with an Illustration of a Study
on Error-Related Potentials
Marco Congedo, Sandra Rousseau and Christian Jutten
Abstract
Over the last twenty years, blind source separation (BSS) has become a
fundamental signal processing tool in the study of human electroencephalography
(EEG), other biological data, as well as in many other signal processing domains
such as speech, images, geophysics, and wireless. This chapter introduces a short
review of brain volume conduction theory, demonstrating that BSS modeling is
grounded on current physiological knowledge. Then, it illustrates a general BSS
scheme requiring the estimation of second-order statistics (SOS) only. A simple
and ef
cient implementation based on the approximate joint diagonalization of
covariance matrices (AJDC) is described. The method operates in the same way in
the time or frequency domain (or both at the same time) and is capable of
modeling explicitly physiological and experimental source of variations with
remarkable
c example illustrat-
ing the analysis of a new experimental study on error-related potentials.
flexibility. Finally, this chapter provides a speci
8.1
Introduction
Over the last twenty years, blind source separation (BSS) has become a funda-
mental signal processing tool in the study of human electroencephalography (EEG),
other biological data, as well as in many other signal processing domains such as
speech, images, geophysics, and wireless communication (Comon and Jutten
2010
). Without relying on head modeling, BSS aims at estimating both the
