  Brain-computer interface (BCI) is used for communication between humans and
devices by recognizing status and intention of humans. Communication between
humans and a drone using electroencephalogram (EEG) signals is one of the most
challenging issues in the BCI domain. In particular, the control of drone
swarms (the direction and formation) has more advantages compared to the
control of a drone. The visual imagery (VI) paradigm is that subjects visually
imagine specific objects or scenes. Reduction of the variability among EEG
signals of subjects is essential for practical BCI-based systems. In this
study, we proposed the subepoch-wise feature encoder (SEFE) to improve the
performances in the subject-independent tasks by using the VI dataset. This
study is the first attempt to demonstrate the possibility of generalization
among subjects in the VI-based BCI. We used the leave-one-subject-out
cross-validation for evaluating the performances. We obtained higher
performances when including our proposed module than excluding our proposed
module. The DeepConvNet with SEFE showed the highest performance of 0.72 among
six different decoding models. Hence, we demonstrated the feasibility of
decoding the VI dataset in the subject-independent task with robust
performances by using our proposed module.
