A brain computer interface (BCI) transforms electrophysiological signals originating from the human brain into commands that control devices or applications. In this way, a BCI provides a new nonmuscular communication channel, which can be extremely useful for people with severe neuromuscular disorders such as amyotrophic lateral sclerosis and brainstem stroke. The immediate goal of our current research is to provide these users with an opportunity to communicate with their environment. Future applications also include the field of multimedia and virtual reality.

Present day BCIs use a variety of electrophysiological signals such as slow cortical potentials, evoked potentials (P300), oscillatory activity recorded from scalp or subdural electrodes, and cortical neuronal activity recorded from implanted electrodes. EEG is by far the most frequently used input source, because it is readily available and noninvasive. The poor signal-to-noise ratio of scalp–recorded signals requires the application of advanced signal processing methods.

This article outlines and explains the current approaches and methods used in BCI research with emphasis on the signal processing part of the system, consisting of preprocessing, feature extraction, and classification. However, the success of a BCI depends not only on the methodologies applied, but also on the capability of the user to develop and maintain the skill to produce the brain patterns employed by the BCI. Therefore, the interaction between the BCI system and the user, in terms of adaptation and learning, is a challenging aspect of any BCI development and application.