Introduction In blind source separation an N-channel sensor signal x(t) arises from M unknown scalar source signals s i (t), linearly mixed together by an unknown N M matrix A, and possibly corrupted by additive noise (t) x(t) = As(t) + (t) (1.1) We wish to estimate the mixing matrix A and the M-dimensional source signal s(t). Many natural signals can be sparsely represented in a proper signal dictionary s i (t) = K X k=1 C ik ' k (t) (1.2) The scalar functions ' k

We applied second-order blind identification (SOBI), an independent component analysis (ICA) method, to MEG data collected during cognitive tasks. We explored SOBI's ability to help isolate underlying neuronal sources with relatively poor signal-to-noise ratios, allowing their identification and localization. We compare localization of the SOBI-separated components to localization from unprocessed sensor signals, using an equivalent current dipole (ECD) modeling method. For visual and somatosensory modalities, SOBI preprocessing resulted in components that can be localized to physiologically and anatomically meaningful locations.

Applying the time-delayed decorrelation (TDD) algorithm to raw data from a visual stimulation magnetoencephalographic (MEG) experiment we investigate the viability of classification of components into artifact or stimulus related components. The TDD components associated with the cardiac artifact show a striking similarity with a Principal Component Analysis (PCA) of the averaged cardiac artifact. This could be due to a violation of the TDD assumptions by the cardiac artifact. Two TDD components have time series peaking consistently at the time expected for the primary response due to the visual stimulation, but their field maps are different from the earliest signal in the average response. An identification of TDD components with physiological sources needs further investigation.

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