Brain-computer interface (BCI) is a technology that controls computers by reflecting users' intentions. Especially the electroencephalogram (EEG)-based BCI systems have been developed because of their potential utility. In BCI studies, controlling the drone swarm is one of the important issues since it improves work efficiency and safety. Also, current research has investigated how the drone swarms are controlled by imagining their formations using visual imagery (VI)-based EEG signals. The raw EEG signals and the spectrogram are widely used as input representations for decoding EEG signals. However, the decoding performance of the VI-based EEG signals is low to control the drone swarm due to noise in the raw EEG signals and information loss problems that may arise in the spectrogram. In this paper, we develop the EEGgram generator that extracts spectrogram-like features from the raw EEG signals minimizing information loss problems. Also, we propose the EEGgramNet, which could extract the significant information from VI-based EEG signals using both the spectrogram and the EEGgram as inputs. The proposed method outperforms an accuracy of 0.643, which is 8.4 % higher than that of the best conventional method. Hence, we demonstrate the possibility of constructing a VI-based BCI system to control the drone swarm by imagining its formations.
|Title of host publication||2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 2022|
|Event||2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022 - Prague, Czech Republic|
Duration: 2022 Oct 9 → 2022 Oct 12
|Name||Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics|
|Conference||2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022|
|Period||22/10/9 → 22/10/12|
Bibliographical noteFunding Information:
*This research was supported by the Challengeable Future Defense Technology Research and Development Program(912911601) of Agency for Defense Development in 2020.
© 2022 IEEE.
- Brain-computer interface (BCI)
- Drone swarm
- Electroencephalogram (EEG)
- Visual imagery
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Control and Systems Engineering
- Human-Computer Interaction