Independent component analysis for EEG source localization

We consider a spatiotemporal method for source localization, taking advantage of the entire EEG time series to reduce the configuration space we must evaluate. The EEG data are 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.

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