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.