  Electroencephalogram (EEG) based brain-computer interface (BCI) systems are
useful tools for clinical purposes like neural prostheses. In this study, we
collected EEG signals related to grasp motions. Five healthy subjects
participated in this experiment. They executed and imagined five
sustained-grasp actions. We proposed a novel data augmentation method that
increases the amount of training data using labels obtained from electromyogram
(EMG) signals analysis. For implementation, we recorded EEG and EMG
simultaneously. The data augmentation over the original EEG data concluded
higher classification accuracy than other competitors. As a result, we obtained
the average classification accuracy of 52.49% for motor execution (ME) and
40.36% for motor imagery (MI). These are 9.30% and 6.19% higher, respectively
than the result of the comparable methods. Moreover, the proposed method could
minimize the need for the calibration session, which reduces the practicality
of most BCIs. This result is encouraging, and the proposed method could
potentially be used in future applications such as a BCI-driven robot control
for handling various daily use objects.
