Journal
2021 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
Volume -, Issue -, Pages 3396-3401Publisher
IEEE
DOI: 10.1109/SMC52423.2021.9659151
Keywords
Brain-computer interface (BCI); Electroencephalogram (EEG); Subject-independent task; Visual imagery (VI); Deep convolutional neural network
Funding
- Defense Challengeable Future Technology Program of Agency for Defense Development, Republic of Korea
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The study introduced the SEFE method to enhance performance in subject-independent tasks using the VI dataset, demonstrating the potential for generalization among various subjects in VI-based BCI.
Brain-computer interface (BCI) is used for communication between humans and devices by recognizing humans' status and intention. 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 subjects' EEG signals 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.
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