Non-Negative Matrix Factorization-Based EEG Signal Classification

Feature extraction and classification of electroencephalogram (EEG) signal for normal and epileptic patients is one of the most challenging research areas in the field of biomedical signal processing. Epileptic seizures are manifestations of epilepsy. Several techniques have been used for feature extraction and classification of EEG in last decades. In this article, non-negative matrix factorization (NMF) has been used for feature extraction of EEG signals and the classifiers used are Artificial Neural Network (ANN) and support vector machine (SVM). For NMF, fast Fourier transform (FFT) is used for feature extraction of the EEG signal. Performances of classifiers are measured on the parameters: accuracy, sensitivity and specificity. It has been observed that NMF gives satisfactory results for feature extraction and classification of EEG signal.