EEG Spectral Connectivity Analysis in a Large Clinical Population

Abstract

This study explores neural connectivity in resting state through coherence and spectral graph based methods across large populations with electroencephalography (EEG). Using the Neural Engineering Data Consortium (NEDC) EEG Corpus we extract EEG data in a 10-20 montage and accompanying patient characteristics. Non-medicated subjects with clinically normal EEG are used as the normative population (n=1,167) while a group with a similar age distribution of medicated subjects with clinically abnormal EEG are used as the abnormal population (n=2,940). Parameters and properties of spectral coherence connectivity graphs are computed across frequency bands. We establish default mode networks (DMN) for the different populations on several frequency bands. We find that frequency bands differ across the populations more than specific graph properties. However, we find that there is an increased level of connectivity in the abnormal population. These results may lead to neural connectivity based diagnostic aides.