Many brain–computer interface (BCI) studies overlook the channel optimization due to its inherent complexity. However, a careful selection increases performance and users’ comfort while reducing cost of system. Evolutionary meta-heuristics, which have demonstrated their usefulness in solving complex problems, not been fully exploited yet this context. The purpose study is two-fold: (1) propose ...

We propose a BCI mouse and speller based on the manipulation of P300 waves in EEG signals. The 2–D motion of the pointer on the screen is controlled by directly combining the amplitudes of the output produced by a filter in the presence of different stimuli. This filter and the features to be combined within it are optimised by a GA.

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...