Nonnegative Tensor Decomposition for EEG Epileptic Spike Detection

Abstract

Tensor decomposition can be used for analyzing multi- channel EEG signals in epilepsy diagnosis. We propose a new tensor-based approach to detect epileptic spikes in EEG data. Nonnegative Tucker decomposition was applied to analyze multi-domain features of EEG epileptic and non-epileptic spikes. An EEG feature extraction method was proposed, based on estimating the so-called “eigenspikes.” The Fisher score was employed for feature selection. KNN and NB classifiers were used on the extracted features to separate epileptic spikes from non- epileptic spikes, and classification results were compared with those of the Phan-Cichoki method. Experimental results showed that our proposed method is efficient in detecting epileptic spikes.