Brain-computer interfaces (BCIs) aim to provide a new channel of communication by enabling the subject to control an external systems by using purely mental commands. One method of doing this without invasive surgical procedures is by measuring the electrical activity of the brain on the scalp through electroencephalography (EEG). A major obstacle to developing complex EEG-based BCI systems that provide a number of intuitive mental commands is the high variability of EEG signals. EEG signals from the same subject vary considerably within a single session and between sessions on the same or different days. To deal with this we are investigating methods of adapting the classifier while it is being used by the subject. By keeping the classifier constantly tuned to the EEG signals of the current session we hope to improve the performance of the classifier and allow the subject to learn to use the BCI more effectively. This paper discusses preliminary offline and online experiments towards this goal, focusing on the initial training period when the task that the subject is trying to achieve is known and thus supervised adaptation methods can be used. In these experiments the subjects were asked to perform three mental commands (imagination of left and right hand movements, and a language task) and the EEG signals were classified with a Gaussian classifier.