Decomposing EEG data into space-time-frequency components using parallel factor analysis and its relation with cerebral blood flow

Abstract

Finding the means to efficiently summarize electroencephalographic data has been a long-standing problem in electrophysiology. Our previous works showed that Parallel Factor Analysis (PARAFAC) can effectively perform atomic decomposition of the time-varying EEG spectrum in space/ frequency/time domain. In this study, we propose to use PARAFAC for extracting significant activities in EEG data that is concurrently recorded with functional Magnetic Resonance Imaging (fMRI), and employ the temporal signature of the atom for investigating the relation between brain electrical activity and the changing of BOLD signal that reflects cerebral blood flow. We evaluated the statistical significance of dynamical effect of BOLD respect to EEG based on the modeling of BOLD signal by plain autoregressive model (AR), its AR with exogenous EEG input (ARX) and ARX with nonlinear term (ARNX). © 2008 Springer-Verlag Berlin Heidelberg.