Unsupervised brain computer interface based on inter-subject information.

This paper presents an unsupervised subject modeling technique and its application to a P300-based word speller. Due to EEG variations across subjects, a special training procedure is required to learn a subject-specific classification model (SSCM). To deal with the inter-subject variation, we first study a subject independent classification model (SICM) that is learned from EEG of a pool of subjects. Next we further adapt the SICM by learning from a subset of the pooled EEG that is automatically selected based on its similarity to the EEG of a new subject. Experiments over ten healthy subjects show that the SICM learned from all pooled EEG outperforms the cross-subject models greatly. More importantly, the adapted SICM achieves virtually the same performance as the SSCM, hence removing the complicated and tedious training procedure.