  In this study, we adopted visual motion imagery, which is a more intuitive
brain-computer interface (BCI) paradigm, for decoding the intuitive user
intention. We developed a 3-dimensional BCI training platform and applied it to
assist the user in performing more intuitive imagination in the visual motion
imagery experiment. The experimental tasks were selected based on the movements
that we commonly used in daily life, such as picking up a phone, opening a
door, eating food, and pouring water. Nine subjects participated in our
experiment. We presented statistical evidence that visual motion imagery has a
high correlation from the prefrontal and occipital lobes. In addition, we
selected the most appropriate electroencephalography channels using a
functional connectivity approach for visual motion imagery decoding and
proposed a convolutional neural network architecture for classification. As a
result, the averaged classification performance of the proposed architecture
for 4 classes from 16 channels was 67.50 % across all subjects. This result is
encouraging, and it shows the possibility of developing a BCI-based device
control system for practical applications such as neuroprosthesis and a robotic
arm.

Signal Processing;Neural and Evolutionary Computing