title: Adaptive active auditory brain computer interface.
abstract: 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.
authors: Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China.
authors: Bo
authors: B
authors: Hong
authors: Bin
authors: B
authors: Lou
authors: Jing
authors: J
authors: Guo
authors: Shangkai
authors: S
authors: Gao
mesh_terms: 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