Abstract
In this study, we used canonical correlation analysis (CCA), frequency component correlation method (FCCM) and ensemble model to develop a steady state visual evoked potential brain-computer interface (SSVEP-BCI) with fifty-selective. For the proposed fifty-selective SSVEP-BCI with only CCA, it was not possible to obtain sufficient SSVEP induction. In a previous study where a similar problem occurred, the maximum accuracy was 79.53%, and the information transfer rate (ITR) was 45.16 bits/min. Therefore, we proposed FCCM and ensemble model in CCA to improve the accuracy even when the SSVEP induction was not sufficient. We used the proposed method to achieve the highest accuracy of 93.23% and the highest ITR of 58.88 bits/min. The system also achieved an average accuracy of 71.01% and an average ITR of 40.79 bits/min, demonstrating the usefulness of the system. Also, the maximum accuracy and ITR in additional experiments were 98.53% and 65.41 bits/min.
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