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EEG-based Classification of Epileptic and Non-Epileptic Events using Multi-Array Decomposition

In this paper, the classification of epileptic and non-epileptic events from EEG is investigated based on temporal and spectral analysis and two different schemes for the formulation of the training set. Although matrix representation which treats features as concatenated vectors allows capturing dependencies across channels, it leads to significant increase of feature vector dimensionality and lacks a means of modeling dependencies between features. Thus, the authors compare the commonly used matrix representation with a tensor-based scheme. TUCKER decomposition is applied to learn the essence of original, high-dimensional domain of feature space. In contrast to other relevant studies, the authors extend the non-epileptic class to both psychogenic non-epileptic seizure and vasovagal syncope. The classification schemes were evaluated on EEG epochs from 11 subjects. The proposed tensor scheme achieved an accuracy of 97,7% which is better compared to the spatiotemporal model even after trying to improve the latter by dimensionality reduction through principal component analysis and feature selection.