Jacobi Angles for Simultaneous Diagonalization
SIAM Journal on Matrix Analysis and Applications
A fast fixed-point algorithm for independent component analysis
Neural Computation
Independent component analysis for identification of artifacts in magnetoencephalographic recordings
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Analyzing and visualizing single-trial event-related potentials
Proceedings of the 1998 conference on Advances in neural information processing systems II
Blind Source Separation by Sparse Decomposition in a Signal Dictionary
Neural Computation
Source localization using recursively applied and projected (RAP)MUSIC
IEEE Transactions on Signal Processing
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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. Furthermore. this preproeessing allowed the detection of neuronal source activations that were otherwise undetectable. This increased probability of neuronal source detection and localization can be particularly beneficial for MEG studies of higher level cognitive functions, which often have greater signal variability and degraded signal-to-noise ratios than sensory activation tasks.