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Independent Components of Magnetoencephalography: Localization

akaysha{at}unm.edu, Departments of Psychology, Computer Science, and Neurosciences, University of New Mexico, Albuquerque, NM 87131, U.S.A.

Barak A. Pearlmutter

bap{at}cs.unm.edu, Departments of Computer Science and Neurosciences, University of New Mexico, Albuquerque, NM 87131, U.S.A.

Natalie A. Malaszenko

natmalas{at}aol.com, Department of Psychology, University of New Mexico, Albuquerque, NM 87131, U.S.A.

Dan B. Phung

danp{at}page1book.com, Department of Biology, University of New Mexico, Albuquerque, NM 87131, U.S.A.

Bethany C. Reeb

breeb{at}unm.edu, Department of Psychology, University of New Mexico, Albuquerque, NM 87131, U.S.A.

We applied second-order blind identification (SOBI), an independent component analysis 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 modeling method. For visual and somatosensory modalities, SOBI preprocessing resulted in components that can be localized to physiologically and anatomically meaningful locations. Furthermore, this preprocessing 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.

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