Abstract
Brain computer interface (BCI) enables motion-free communication between humans and human and machines. In particular, BCIs using visual stimuli (Visual-BCI) have been successfully developed in the speed and accuracy. However, there exists a crucial problem that visually impaired people cannot use the Visual-BCIs. It is thus useful to develop eye-independent BCIs which exploit other modalities than vision. We focus on a BCI using amplitude modulated auditory stimuli which elicit auditory steady-state response (ASSR) as one of the eye-independent BCIs. Conventional auditory BCIs based on ASSR (ASSR- BCI) have had low performance due to nonperiodic fluctuation and small amplitude in ASSR. The present study was aimed at improving the performance of ASSR-BCI using neural network. As a result, the discrimination accuracy of the proposed ASSR-BCI was improved by 11% compared to the conventional ASSR-BCI which used a support vector machine classifier.
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