—This paper describes the voluntary eye blink
detection method using electrooculogram (EOG). There
are still challenge problems to put brain-computer interface
(BCI) systems to real-life applications. In general BCI systems,
there is a possibility of incorrect and unintentional
input because input is automatically selected even if the
requirements are accidentally met. This study aims to propose
the voluntary eye blink detection method and apply
it to the trigger switch of BCI systems. In the proposed
method, normal blink, double blink, and wink can be detected
from vertical and horizontal EOG signals. We employed
the positive peak of vertical and horizontal amplitude
and maximum cross correlation coefficient between
vertical EOG and template signal of double blink in feature
extraction. Eye blinks were classified by support vector
machine. As the result of simulations, an average accuracy
of 97.28% was obtained using our method. In addition,
the best accuracy for voluntary eye blinks was obtained for
wink with accuracy of 97.69 %. This paper proof wink
is suitable for trigger switch of BCI system, and online
method for voluntary eye blink detection.