Neural network classification of EEG signals using time-frequency representation

This paper addresses the problem of classification of electroencephalogram (EEG) signals obtained from human subjects performing two mental tasks. One task named baseline involves relaxing and thinking of nothing in particular and the other task named multiplication involves mentally multiplying two 2-digit integers. First, the EEG signals are pre-processed using independent component analysis for removal of artifacts. Then, a time-frequency representation of the signals is generated, from which wavelet-based texture features are extracted for classification. The texture features are fed into a three-layer neural network classifier trained by the backpropagation algorithm. A classification rate of 96% is obtained for the dataset examined. The entire classification system has been implemented in the LabVIEW graphical programming environment providing a user-friendly interface to alter and monitor various parameters of the neural network classifier.