EEG source localization for brain-computer-interfaces

While most EEG based Brain-Computer-Interfaces (BCIs) employ machine learning algorithms for classification, we propose to utilize source localization procedures for this purpose. Although the computational demand is considerably higher, this approach could allow the simultaneous classification of a multitude of conditions. We present an extension of Independent Component Analysis (ICA) - based source localization that is fully automatic, and apply this method to the classification of EEG data generated by imaginary movements of the right and left index finger. The results demonstrate that source localization provides a viable alternative to machine learning algorithms for BCIs.