Determination of an optimal training strategy for a BCI classification task with LDA

Brain computer interfaces (BCIs) translate brain activity into computer commands. To enhance the performance of a BCI, it is necessary to improve the feature extraction techniques being applied to decode the users' intentions. Objective comparison methods are needed to analyze different feature extraction techniques. One possibility is to use the classifier performance as a comparative measure. In this paper, we study the behavior of linear discriminant analysis (LDA) when used to distinguish between electroencephalographic (EEG) signals with and without the presence of event related potentials (ERPs).

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