Classification of Brain-Computer Interface Data

In this paper we investigate the classification of mental tasks based on electroencephalographic (EEG) data for Brain Computer Interfaces (BCI) in two scenarios: off line and on-line. In the off-line scenario we evaluate the performance of a number of classifiers using a benchmark dataset, the same pre-processing and feature selection and show that classifiers that haven't been used before are good choices. We also apply a new feature selection method that is suitable for the highly correlated EEG data and show that it greatly reduces the number of features without deteriorating the classification accuracy. In the on-line scenario that we have designed, we study the performance of our system to play a computer game for which the signals are processed in real time and the subject receives visual feedback of the resulting control within the game environment. We discuss the performance and highlight important issues.

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