  Brain-computer interfaces (BCIs) use brain signals such as
electroencephalography to reflect user intention and enable two-way
communication between computers and users. BCI technology has recently received
much attention in healthcare applications, such as neurorehabilitation and
diagnosis. BCI applications can also control external devices using only brain
activity, which can help people with physical or mental disabilities,
especially those suffering from neurological and neuromuscular diseases such as
stroke and amyotrophic lateral sclerosis. Motor imagery (MI) has been widely
used for BCI-based device control, but we adopted intuitive visual motion
imagery to overcome the weakness of MI. In this study, we developed a
three-dimensional (3D) BCI training platform to induce users to imagine
upper-limb movements used in real-life activities (picking up a cell phone,
pouring water, opening a door, and eating food). We collected intuitive visual
motion imagery data and proposed a deep learning network based on functional
connectivity as a mind-reading technique. As a result, the proposed network
recorded a high classification performance on average (71.05%). Furthermore, we
applied the leave-one-subject-out approach to confirm the possibility of
improvements in subject-independent classification performance. This study will
contribute to the development of BCI-based healthcare applications for
rehabilitation, such as robotic arms and wheelchairs, or assist daily life.
