  The electroencephalogram, a type of non-invasive-based brain signal that has
a user intention-related feature provides an efficient bidirectional pathway
between user and computer. In this work, we proposed a deep learning framework
based on corresponding frequency empahsize method to decode the motor imagery
(MI) data from 2020 International BCI competition dataset. The MI dataset
consists of 3-class, namely 'Cylindrical', 'Spherical', and 'Lumbrical'. We
utilized power spectral density as an emphasize method and a convolutional
neural network to classify the modified MI data. The results showed that
MI-related frequency range was activated during MI task, and provide
neurophysiological evidence to design the proposed method. When using the
proposed method, the average classification performance in intra-session
condition was 69.68% and the average classification performance in
inter-session condition was 52.76%. Our results provided the possibility of
developing a BCI-based device control system for practical applications.
