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.
Lu
Shijian
S
Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613. slu@i2r.a-star.edu.sg
Guan
Cuntai
C
Zhang
Haihong
H
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014584:User-Computer Interface