A hybrid cascade method for EEG classification

The classification of EEG signals is an essential step in the design of brain–computer interface. In order to optimize this step, a method for EEG classification using a cascade technique is proposed. This classification process is implemented in two steps. In the first stage, the feature extraction is accomplished using a discrete wavelet transform (DWT), followed by a feature selection via a filter method in order to select pertinent features from the original feature set. Therefore, reduced feature vectors are presented as input to the classifier. In the second step, features are extracted from the misclassified trials resulting from the first step. Then, the most relevant features are selected. Eventually, the feature vectors are classified. Two configurations of cascade classifiers are tested. In each of them, the classifier used in the first step of the classification scheme is different from the one applied in the second step. Moreover, regarding feature extraction, two cases are studied. In the first case, the same wavelet function is applied in the two steps of the classification process, whereas in the second case, two different wavelet functions are applied successively in the two steps of the classification procedure. The proposed classification scheme was applied on EEG signals of motor imagery dataset III available in BCI competition II, leading to good classification results compared to the existent methods.