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Brain-computer interface (BCI) is used for communication between humans anddevices by recognizing status and intention of humans. Communication betweenhumans and a drone using electroencephalogram (EEG) signals is one of the mostchallenging issues in the BCI domain. In particular, the control of droneswarms (the direction and formation) has more advantages compared to thecontrol of a drone. The visual imagery (VI) paradigm is that subjects visuallyimagine specific objects or scenes. Reduction of the variability among EEGsignals of subjects is essential for practical BCI-based systems. In thisstudy, we proposed the subepoch-wise feature encoder (SEFE) to improve theperformances in the subject-independent tasks by using the VI dataset. Thisstudy is the first attempt to demonstrate the possibility of generalizationamong subjects in the VI-based BCI. We used the leave-one-subject-outcross-validation for evaluating the performances. We obtained higherperformances when including our proposed module than excluding our proposedmodule. The DeepConvNet with SEFE showed the highest performance of 0.72 amongsix different decoding models. Hence, we demonstrated the feasibility ofdecoding the VI dataset in the subject-independent task with robustperformances by using our proposed module.

Subject-Independent Brain-Computer Interface for Decoding High-Level Visual Imagery Tasks