Difficulties Applying Recent Blind Source Separation Techniques to Eeg And

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. Propagation and mixing of signals from simultaneous active sources occurs in many physical situations. Probably the most familiar situation is the "cocktail party problem" where there are many speakers, or sources of acoustic signals, and the listener detects mixtures of these signals. While the listener has only two ears, we will consider cases that are much less difficult where there are as many detectors as sources. Often we are searching for a signal embedded in noise of some sort. In cases where much information is known about the signal, such as temporal behavior or frequency content, there are useful standard techniques that can assist in isolating the signal. However, when little specific information is known about the temporal or frequency structure or