Empirical Mode Decomposition based Feature Extraction Method for the Classification of EEG Signal

Disease identification is a major task in the field of biomedical. To perform it the analysis of EEG signal is to be performed. The proposed method presents for feature extraction from electroencephalogram (EEG) signals using empirical mode decomposition (EMD). Its use is motivated by the fact that the EMD gives an effective time-frequency analysis of nonstationary signals. The intrinsic mode functions (IMF) obtained as a result of EMD give the decomposition of a signal according to its frequency components. In this present the research of upto third order temporal moments, and spectral features including spectral centroid, coefficient of variation and the spectral skew of the IMFs for feature extraction from EEG signals. Features are physiologically relevant to normal EEG signals. Normal EEG signals have different temporal and spectral centroids, dispersions and symmetries. The performance of the proposed method is studied on a publicly available dataset which is designed to handle various classification problems including the identification of epilepsy patients also detection of seizures and non-seizures. The calculated features are fed into the standard support vector machine (SVM) for classification purposes. The Experimental results show that good classification results are obtained using the proposed methodology for the classification of EEG signals. KeywordsEempirical mode decomposition, intrinsic mode function, feature extraction, classification. __________________________________________________*****_________________________________________________