Abstract
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.