A Novel Learning Classification Scheme for Brain EEG Patterns

EEG has been extensively used to aid the diagnosis of various brain disorders and also, for the identification of brain activities during cognitive tasks. However, the visual evaluation of EEG recordings is a demanding process, susceptible to error and bias due to the human factor involved. The development of EEG analysis methods coupled with data processing and mining techniques have assisted the feature extraction process from EEG recordings. In this paper, a novel method for classification of EEG signals based on features derived from the EEG morphology is proposed. The classification accuracy, as illustrated through experiment evaluation, shows that the proposed method can achieve adequate results and moreover the extracted features can be used collaboratively with commonly used features from time and time-frequency domain to increase the EEG signal's classification performance.