Subject-adaptive steady-state visual evoked potential detection for brain-computer interface

We report on the development of a four command Brain-Computer Interface (BCI) based on steady-state visual evoked potential (SSVEP) responses detected from human electroencephalograms (EEGs). The proposed system combines spatial filtering, feature extraction and selection, and a classifier. Two types of classifiers were compared: one based on equal treatment of all harmonics in all EEG channels and the second based on preliminary training resulting in a weighted treatment of the harmonics. Results from six healthy subjects are evaluated.