TY - GEN
T1 - Subject-Independent Brain-Computer Interface for Decoding High-Level Visual Imagery Tasks
AU - Lee, Dae Hyeok
AU - Han, Dong Kyun
AU - Kim, Sung Jin
AU - Jeong, Ji Hoon
AU - Lee, Seong Whan
N1 - Funding Information:
*This research was supported by the Defense Challengeable Future Technology Program of Agency for Defense Development, Republic of Korea.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Brain-computer interface (BCI)
KW - Deep convolutional neural network
KW - Electroencephalogram (EEG)
KW - Subject-independent task
KW - Visual imagery (VI)
UR - http://www.scopus.com/inward/record.url?scp=85124285027&partnerID=8YFLogxK
U2 - 10.1109/SMC52423.2021.9659151
DO - 10.1109/SMC52423.2021.9659151
M3 - Conference contribution
AN - SCOPUS:85124285027
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 3396
EP - 3401
BT - 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
Y2 - 17 October 2021 through 20 October 2021
ER -