Adaptive active auditory brain computer interface.
An active paradigm was employed to produce reliable and prominent target response in an auditory brain computer interface (BCI), in which subject's voluntary recognition of the property of a target human voice enhances the discriminability between target and non-target EEG response. Furthermore, to adaptively decide the optimal number of trials being averaged for SVM classification, a statistical approach was proposed to convert each sample's margin in support vector space into probabilities of each voice choice being the target. In a testing of 8 subjects' EEG data from the active auditory BCI experiment, the proposed adaptive approach needs only about 4-6 trials to reach the equivalent accuracy of 15-trial averaging. The improved information transfer rate suggests the advantage of adaptive strategy in an active auditory BCI.
Hong
Bo
B
Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China.
Lou
Bin
B
Guo
Jing
J
Gao
Shangkai
S
D000465:Algorithms; D001185:Artificial Intelligence; D004569:Electroencephalography / Q000379:methods*; D005072:Evoked Potentials, Auditory / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D012656:Self-Help Devices; D014584:User-Computer Interface; D014831:Voice / Q000502:physiology