  Developing electroencephalogram (EEG) based brain-computer interface (BCI)
systems is challenging. In this study, we analyzed natural grasp actions from
EEG. Ten healthy subjects participated in this experiment. They executed and
imagined three sustained grasp actions. We proposed a novel approach which
estimates muscle activity patterns from EEG signals to improve the overall
classification accuracy. For implementation, we have recorded EEG and
electromyogram (EMG) simultaneously. Using the similarity of the estimated
pattern from EEG signals compare to the activity pattern from EMG signals
showed higher classification accuracy than competitive methods. As a result, we
obtained the average classification accuracy of 63.89($\pm$7.54)% for actual
movement and 46.96($\pm$15.30)% for motor imagery. These are 21.59% and 5.66%
higher than the result of the competitive model, respectively. 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.
