Relating measures of electroencephalography (EEG) back to the underlying sources is a long-standing inverse problem. Here we propose a new method to estimate the EEG sources of identified electrophysiological states that represent spontaneous activity, or are evoked by a stimulus, or caused by disease or disorder. Our method has the unique advantage of seamlessly integrating a statistical significance of the source estimate while efficiently eliminating artifacts (e.g., due to eye blinks, eye movements, bad electrodes). After determining the electrophysiological states in terms of stable topographies using established methods (e.g.: ICA, PCA, k-means, epoch average), we propose to estimate these states' time courses through spatial regression of a General Linear Model (GLM). These time courses are then used to find EEG sources that have a similar time-course (using temporal regression of a second GLM). We validate our method using both simulated and experimental data. Simulated data allows us to assess the difference between source maps obtained by the proposed method and those obtained by applying conventional source imaging of the state topographies. Moreover, we use data from 7 epileptic patients (9 distinct epileptic foci localized by intracranial EEG) and 2 healthy subjects performing an eyes-open/eyes-closed task to elicit activity in the alpha frequency range. Our results indicate that the proposed EEG source imaging method accurately localizes the sources for each of the electrical brain states. Furthermore, our method is particularly suited for estimating the sources of EEG resting states or otherwise weak spontaneous activity states, a problem not adequately solved before.© 2014 Elsevier Inc. All rights reserved.
IntroductionThe study of brain function has benefited enormously from modern neuroimaging techniques to reveal localization and dynamics of neuronal activity during evoked and spontaneous states. One of the most widely used methodologies to analyze data from functional magnetic resonance imaging (fMRI), is the General Linear Model (GLM) where pre-defined hemodynamic responses are used in a linear regression model and contrasts of interest are evaluated by statistical hypothesis testing (Bandettini et al., 1992;Friston et al., 1995;Kwong et al., 1992;Ogawa et al., 1992). As the fMRI signal is related to neuronal activity via neurovascular coupling, it only provides a (slow) proxy for neuronal activity. Electroencephalography (EEG), on the other hand, directly records the fast changes of current potential related to neuronal activity. Recent advances in high-density recording and 3D source analysis have increased EEG accuracy as a brain imaging method with the inherent advantage of high temporal resolution (Michel and Murray, 2012) It is fairly natural to conceive the application of conventional GLM analysis as used for fMRI to the Electrical Source Images of the EEG (or ESI: with this general term we indicate any method mapping scalp measurements into source space), as some previous papers (Bro...