  Brain-computer interface (BCI) systems are usually designed specifically for
each subject based on motor imagery. Therefore, the usability of these networks
has become a significant challenge. The network has to be designed separately
for each user, which is time-consuming for the user. Therefore, this study
proposes a method by which the calibration time is significantly reduced while
the classification accuracy is increased. In this method, we calibrated the
features extracted from the motor imagery task by dividing the features
extracted from the resting-state into both open-eye and closed-eye modes and
the state in which the subject moves his eyes. The best classification accuracy
was obtained using the SVM classifier using the resting-state signal in the
open eye, which increased by 3.64% to 74.04%. In this paper, we also
investigated the effect of recording time of the resting-state signal and the
impact of eye state on the classification accuracy.
