Objective selection of epilepsy-related independent components from EEG data

Abstract

Background: Independent Component Analysis (ICA) is commonly used for the identification of sources of interest in electroencephalographic (EEG) data, but the selection of the relevant components remains an open issue depending on the specific application.

New method: We propose a novel approach for the objective selection of epilepsy-related independent components (ICs) from EEG data collected during functional Magnetic Resonance Imaging (fMRI) acquisitions, called PROJection onto Independent Components (PROJIC). Inter-ictal epileptiform discharges (IEDs) are identified on a reference EEG dataset collected outside the MRI scanner by an expert neurophysiologist, and the resulting average IED is projected onto the IC space of the EEG data collected simultaneously with fMRI. The power of the IED projection is then used to inform a k-means clustering algorithm of the ICs, allowing for the classification of epilepsy-related ICs.

Comparison with existing methods: The performance of PROJIC was compared with two methods previously proposed for the objective selection of EEG ICs of interest, which are based on the explicit similarity of the ICs with spatio-temporal templates of the events of interest, instead of the projection power.

Results: The proposed PROJIC method outperformed the others for both artificial and real data (19 datasets collected from 6 patients with drug-refractory focal epilepsy), with an average accuracy of 98.6%.

Conclusions: The ability of our method to accurately and objectively select epilepsy-related ICs makes it an important contribution for simultaneous EEG-fMRI epilepsy studies, with potential applications in the analysis of event-related EEG activity more generally, and also in EEG artefact correction.

Keywords: Electroencephalography; Epilepsy; Independent component analysis; fMRI.