Classification of EEG Signals from Four Subjects During Five Mental Tasks
Charles W. Anderson · 2007
Neural networks are trained to classify half-second segments of six-channel, EEG data into one of five classes corresponding to five cognitive tasks performed by four subjects. Two and three-layer feedforward neural networks are trained using 10-fold cross-validation and early stopping to control over-fitting. EEG signals were represented as autoregressive (AR) models. The average percentage of test segments correctly classified ranged from 71% for one subject to 38% for another subject. Cluster analysis of the resulting neural networks' hidden-unit weight vectors identifies which EEG channels are most relevant to this discrimination problem. 1 Introduction Visual inspection of multiple time series of EEG signals in their unprocessed form is still the predominant way of discriminating and classifying EEG patterns in the medical community and requires highly trained medical professionals. Since the early days of automatic EEG processing, representations based on a Fourier transform ha...