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In this study, we adopted visual motion imagery, which is a more intuitivebrain-computer interface (BCI) paradigm, for decoding the intuitive userintention. We developed a 3-dimensional BCI training platform and applied it toassist the user in performing more intuitive imagination in the visual motionimagery experiment. The experimental tasks were selected based on the movementsthat we commonly used in daily life, such as picking up a phone, opening adoor, eating food, and pouring water. Nine subjects participated in ourexperiment. We presented statistical evidence that visual motion imagery has ahigh correlation from the prefrontal and occipital lobes. In addition, weselected the most appropriate electroencephalography channels using afunctional connectivity approach for visual motion imagery decoding andproposed a convolutional neural network architecture for classification. As aresult, the averaged classification performance of the proposed architecturefor 4 classes from 16 channels was 67.50 % across all subjects. This result isencouraging, and it shows the possibility of developing a BCI-based devicecontrol system for practical applications such as neuroprosthesis and a roboticarm.

Decoding of Intuitive Visual Motion Imagery Using Convolutional Neural Network under 3D-BCI Training Environment