Brain-Computer Interface (BCI), based on motor imagery, translates the motor intention into a control signal by classifying the electrophysiological patterns of different imagination tasks using ECoG, which can capture a broader range of frequencies showing better sensitivity and higher input quality than EEG. However, with ECoG being an invasive technique, there may be some utility in developing an ECoG bidirectional classifier that reduces the number of implanted electrodes. The present study aims to develop an ECoG signals classifier to achieve high accuracy with a limited number of electrodes.