Towards asynchronous brain-computer interfaces: a P300-based approach with statistical models.
Asynchronous control is a critical issue in developing brain-computer interfaces for real-life applications, where the machine should be able to detect the occurrence of a mental command. In this paper we propose a computational approach for robust asynchronous control using the P300 signal, in a variant of oddball paradigm. First, we use Gaussian models in the support vector margin space to describe various types of EEG signals that are present in an asynchronous P300-based BCI. This allows us to derive a probability measure of control state given EEG observations. Second, we devise a recursive algorithm to detect and locate control states in ongoing EEG. Experimental results indicate that our system allows information transfer at approx. 20bit/min at low false alarm rate (1/min).
Zhang
Haihong
H
Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore. hhzhang@i2r.a-star.edu.sg
Wang
Chuanchu
C
Guan
Cuntai
C
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods*; D018913:Event-Related Potentials, P300 / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014584:User-Computer Interface