

Next: Introduction Independent Component Analysis For EEG Source Localization In Realistic Head ModelsAbstract:A pervasive problem in neuroscience is determining
which regions of the brain are active, given voltage measurements at the
scalp. If accurate solutions to such problems could be obtained, neurologists
would gain noninvasive access to patientspecific cortical activity. Access
to such data would ultimately increase the number of patients who could
be effectively treated for neural pathologies such as multifocal epilepsy.
However, estimating the location and distribution of electric current source within the brain from electroencephalographic (EEG) recordings is an illposed problem. Specifically, there is no unique solution and solutions do not depend continuously on the data. The illposedness of the problem makes finding the correct solution a challenging analytic and computational problem. In this paper we consider a spatiotemporal method for sources localization, taking advantage of the entire EEG time series to reduce the configuration space we must evaluate. The EEG data is first decomposed into signal and noise subspaces using a Principal Component Analysis (PCA) decomposition. This partitioning allows us to easily discard the noise subspace, which has two primary benefits: the remaining signal is less noisy, and it has lower dimensionality. After PCA, we apply Independent Component Analysis (ICA) on the signal subspace. The ICA algorithm separates multichannel data into activation maps due to temporally independent stationary sources. For each activation map we perform an EEG source localization procedure, looking only for a single dipole per map. By localizing multiple dipoles independently, we substantially reduce our search complexity and increase the likelihood of efficiently converging on the correct solution. Keywords: EEG, ICA, PCA, source localization, realistic head model
Zhukov Leonid Fri Oct 8 13:55:47 MDT 1999 

