  An electroencephalogram is an effective approach that provides a
bidirectional pathway between user and computer in a non-invasive way. In this
study, we adopted the visual perception data for training the visual imagery
decoding network. We proposed a visual perception-guided network training
approach for decoding visual imagery. Visual perception decreases the power of
the alpha frequency range of the visual cortex over time when the user
performed the task, and visual imagery increases the power of the alpha
frequency range of the visual cortex over time as the user performed with the
task. Generated brain signals when the user performing visual imagery and
visual perception have opposite brain activity tendencies, and we used these
characteristics to design the proposed network. When using the proposed method,
the average classification performance of visual imagery with the visual
perception data was 0.7008. Our results provide the possibility of using the
visual perception data as a guide of the visual imagery classification network
training.
