Difficulties applying recent blind source separation techniques to EEG and MEG

TL;DR

This paper discusses the challenges of applying recent blind source separation methods to EEG and MEG data, highlighting the limitations due to the multimodal nature of neural source distributions and proposing the need for more tailored techniques.

Contribution

The paper analyzes why current blind source separation techniques are ineffective for EEG and MEG, emphasizing the multimodal source distributions and suggesting directions for developing better methods.

Findings

Current techniques assume large kurtosis, which is not true for EEG/MEG.

EEG/MEG source distributions are multimodal, complicating separation.

Existing methods are less effective for neural signals than for natural sounds.

Abstract

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.