Feature extraction of electroencephalogram signals applied to epilepsy

In this work, we proposed an analysis framework for Electroencephalogram (EEG) signals and their classification. The EEGs considered for this study belong to both normal as well as epileptic subjects. After wavelet packet decomposition of EEG signals, three important statistical features such as standard deviation, energy and entropy were computed at different sub-bands decomposition. The most suitable wavelets were selected for processing EEG signals. Linear discriminant analysis and principal component analysis are used to reduce the dimension of data. Feature vectors were used to model and train the efficient Support Vector Machine (SVM) classifier. In this study, we have attempted to improve the computing efficiency as it selects the statistical features and the dimensionality reduction method that can provide an important assistant to neuro-physicians, thus to make their decision on their patients.