On Classifiability of Wavelet Features for EEG-Based Brain-computer Interfaces

Given their multiresolution temporal and spectral locality, wavelets are powerful candidates for decomposition, feature extraction, and classification of non-stationary electroencephalographic (EEG) signals for brain-computer interface (BCI) applications. Wavelet feature extraction methods offer several options through the choice of wavelet families and decomposition architectures. The classification results of EEG signals generated from imagined motor, cognitive, and affective tasks are presented using support vector machine (SVM) classifiers, indicating that these methods are suitable for imagined motor, cognitive and affective classification. Classifier performances of better than 80% for six imagined motor tasks, and for two affective tasks were achieved. Three cognitive tasks were successfully classified with 70% accuracy. The methods can be used with a variety of EEG signal reference methods and electrode placement locations. Wavelet features performed satisfactorily in the presence of noise when the classifiers were presented with contaminated training data.