An Introduction to EEG Source Analysis with an Illustration of a Study on Error-Related Potentials

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 efficient 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 flexibility. Finally, this chapter provides a specific example illustrating the analysis of a new experimental study on error-related potentials.

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