ANALYSIS OF MFCC FEATURES FOR EEG SIGNAL CLASSIFICATION

In this paper, an experimental evaluation of Mel-Frequency Cepstral Coefficients (MFCCs) for use in Electroencephalogram (EEG) signal classification is presented. The MFCC features are tested using CHB-MIT Scalp EEG Database. The objective is to classify the given EEG signal into normal or abnormal that is based on the MFCC representation of EEG signal. Initially, the QRS complex waves are detected using Pan Tompkins algorithm, and then the MFCC features are extracted. The performance of MFCC feature representation is analyzed in the context of an Artificial Neural Network (ANN) classification system in terms of sensitivity and specificity. The performance of EEG classification approach depends on the number of MFCC components used for the classification. When compared with 15 and 35 MFCC components, 25 MFCC components gives better result in terms of sensitivity (98%) and specificity (96%).