Seizure detection with common spatial pattern and Support Vector Machines

This paper extends the use of the Common Spatial Pattern (CSP) algorithm for epileptic Electroencephalography (EEG) seizure detection. The CSP algorithm is applied on EEG signal derivative, which contains reinforced details of the signal. The main idea of the proposed approach is to apply a differentiator on the multi-channel EEG signal, and hence the signal is segmented into overlapping segments. Each segment is projected on a CSP projection matrix to extract the training and testing features. In selecting the training period, a leave-one-hour-out cross validation strategy is adopted. A Support Vector Machine (SVM) classifier is then trained with the training features to classify inter-ictal and ictal segments. Two variants of the CSP are presented and tested in this paper; the original CSP and the Diagonal Loading CSP.

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