Human Brain Mapping

NEw York City, NY

June 27-30, 2003

Scott Makeig, Arnaud Delorme, Jorn Anemuller,

Institute for Neural Computation, UCSD & The Salk Institute, La Jolla CA

Noninvasive imaging of cortical potential flow

Introduction:In nearly all EEG analysis, signal sources are explicitly or implicitly modeled as spatially fixed domains characterized by activity synchronized across the spatial domain of each source. However, optical and electrical grid recordings on cortex of animals often show activity occurring in waves that travel quickly across millimeters of cortex. In a convolutive mixing process, a single EEG component may elicit a sequence of potential maps with varying spatial topography. We reasoned that it may be thereby possible to observe, in non-invasively recorded EEG data, temporally independent patterns of potential source flow on the cortex.

Method:: We developed and applied complex frequency-domain independent component analysis (ICA) (Anemuller et al., 2003) to separate characteristic patterns of cortical potential flow. Input data are transformed into the frequency domain using the standard methods of short-time Fourier transformation or wavelet transformation. Complex sources are modeled by a circularly symmetric non-Gaussian probability density distribution over the complex plane. Maximum likelihood method is used to obtain the complex counterpart of the well-known infomax algorithm for real-valued EEG signals (Makeig et al., 1996) at each frequency of interest. The obtained complex independent component activations and associated complex scalp maps were then visualized as flow patterns.

Results:: Here we report preliminary results of first applications using 32-channel EEG data recorded during attention-demanding tasks. These showed that many components with physiological plausible maps (about half) exhibited a negligible imaginary part clearly deviating from the real/stationary case. Some of the complex component maps, projected back to the real phase domain, resembled physiologically plausible spatiotemporal flows. The figure shows an example of such a dipole path and associated scalp maps at 12 equally-spaced phases of a complex alpha component, with the trajectory of equivalent dipole positions determined using BESA (Scherg, 1986). Complex ICA components also exhibited a higher degree of temporal independence than standard (real) independent components of the same data. By clustering complex components across different frequencies, clusters of frequency bands emerged which were similar to those frequency bands that have long been associated by EEG researchers with different physiological processes.

Discussion:: The complex ICA method appears capable of opening a wholly new and completely noninvasive window into human brain dynamics. The trajectory of equivalent dipole position, as well as of its orientation, should, we expect, fit the curvature of involved cortex. Fitting complex component trajectories to the cortical mantle thus could prove a powerful method for localizing cortical domains. Our method should give a new, non-invasively determined estimator, the 'flow trajectory', of the spatial extent and orientation profile of the coherent cortical domains that generate the scalp EEG. RESULTS: Here we report preliminary results of first applications using 32-channel EEG data recorded during attention-demanding tasks. These showed that many components with physiological plausible maps (about half) exhibited a negligible imaginary part clearly deviating from the real/stationary case. Some of the complex component maps, projected back to the real phase domain, resembled physiologically plausible spatiotemporal flows. The figure shows an example of such a dipole path and associated scalp maps at 12 equally-spaced phases of a complex alpha component, with the trajectory of equivalent dipole positions determined using BESA (Scherg, 1986). Complex ICA components also exhibited a higher degree of temporal independence than standard (real) independent components of the same data. By clustering complex components across different frequencies, clusters of frequency bands emerged which were similar to those frequency bands that have long been associated by EEG researchers with different physiological processes.