Abstract
The N200 speller is a novel brain-computer interface (BCI) paradigm utilizing the overt attention effects on motion onset visual evoked potentials (mVEP). However, the asynchronous performance of the N200 BCI has not been fully explored. In this paper, a novel algorithm was proposed, integrating the spatial profile of the visual speller to provide a more precise description of the mVEP responses. Most importantly, only control state data were used in the algorithm to train a classifier which can detect the non-control state effectively. Using offline recorded data, the asynchronous performance of the proposed algorithm was shown to be significantly better than that of a similar algorithm without using the spatial information. The proposed algorithm can be used for developing a practical, asynchronous N200 BCI system.
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