Abstract: The method of classifying raw EEG signals uses a classification method based on nuclear features extracted as dominant singular values from an EEG signal segment using singular value decomposition (SVD) and a class means-based minimum distance classifier (CMMDC) to classify a patient's EEG signals. From a mean EEG signal, a set of zero-centered EEG signals are calculated, and from the zero-centered EEG signals and a standard deviation of the EEG signals, a unit variance is calculated for each component. Using the standardized component signals a nuclear matrix is calculated, to which singular value decomposition is applied to generate a set of singular values. The CMMDC is applied to class means associated with first and second classes and a nuclear feature vector to classify the patient's EEG signals as belonging in either the first or second class.