PRINCIPAL-COMPONENT LOCALIZATION OF THE SOURCES OF THE BACKGROUND EEG

A method, based on principal components for localizing the sources of the background EEG, is presented which overcomes the previous limitati ons of this approach, The spatiotemporal source model of the EEG is as sumed to apply, and the method involves attempting to fit the spatial aspects of this general model with an optimal rotation of a subset of the principal components of a particular EEG, The method is shown to b e equivalent to the subspace scanning method, a special case of the MU SIC algorithm, which enables multiple sources to be localized individu ally rather than all at once. The novel aspect of the new method is th at it offers a way of selecting the relevant principal components for the localization problem, The relevant principal components are chosen by decomposing the EEG using spatial patterns common with a control E EG, These spatial patterns have the property that they account for max imally different proportions of the combined variances in the two EEG' s, An example is given using a particular EEG from a neurologic patien t. Components containing spike and sharp wave potentials are extracted , with respect to a standard EEG derived from 15 normal volunteers, Sp ike and sharp wave potentials are identified visually using the common spatial patterns decomposition and an EEG reconstructed from these co mponents, Four dipole sources are fitted to the principal components o f the reconstructed EEG and these source account for over 88% of the t emporal variance present in that EEG.