  High temporal resolution measurements of human brain activity can be
performed by recording the electric potentials on the scalp surface
(electroencephalography, EEG), or by recording the magnetic fields near the
surface of the head (magnetoencephalography, MEG). The analysis of the data is
problematic due to the fact that multiple neural generators may be
simultaneously active and the potentials and magnetic fields from these sources
are superimposed on the detectors. It is highly desirable to un-mix the data
into signals representing the behaviors of the original individual generators.
This general problem is called blind source separation and several recent
techniques utilizing maximum entropy, minimum mutual information, and maximum
likelihood estimation have been applied. These techniques have had much success
in separating signals such as natural sounds or speech, but appear to be
ineffective when applied to EEG or MEG signals. Many of these techniques
implicitly assume that the source distributions have a large kurtosis, whereas
an analysis of EEG/MEG signals reveals that the distributions are multimodal.
This suggests that more effective separation techniques could be designed for
EEG and MEG signals.
