  An electroencephalogram is an effective approach that provides a
bidirectional pathway between the user and computer in a non-invasive way. In
this study, we adopted the visual imagery data for controlling the BCI-based
robotic arm. Visual imagery increases the power of the alpha frequency range of
the visual cortex over time as the user performs the task. We proposed a deep
learning architecture to decode the visual imagery data using only two channels
and also we investigated the combination of two EEG channels that has
significant classification performance. When using the proposed method, the
highest classification performance using two channels in the offline experiment
was 0.661. Also, the highest success rate in the online experiment using two
channels (AF3-Oz) was 0.78. Our results provide the possibility of controlling
the BCI-based robotic arm using visual imagery data.
