Fifty-selective SSVEP-BCI Speller with CCA

In this study, we used canonical correlation analysis (CCA), frequency component 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 only CCA, it was not possible obtain sufficient SSVEP induction. previous study where similar problem occurred, maximum accuracy 79.53%, information transfer rate (ITR) 45.16 bits/min. Therefore, FCCM in CCA improve even when induction sufficient. We achieve highest of 93.23% ITR 58.88 The system also achieved an average 71.01% 40.79 bits/min, demonstrating usefulness system. Also, additional experiments were 98.53% 65.41