Evaluating the performance of three feature sets for brain-computer interfaces with an early stopping MLP committee

We present preliminary classification results for a real time brain-computer interface. Our approach seeks to build individual brain interfaces rather than universal ones. This means that the interface should adapt to its owner; as it will incorporate a neural classifier that learns user-specific features. Three feature sets extracted with Fourier transform, autoregressive models and wavelets were evaluated with early stopping MLP committee. The goal was to classify EEG patterns related to imagined hand movements and relax. The best results were obtained with the autoregressive special features. The results so far are not satisfactory for their intended use as basis for robust EEG classification but they give us valuable basis for future work