An EEGgram-based Neural Network Enhancing the Decoding Performance of Visual Imagery EEG Signals to Control the Drone Swarm

Sung Jin Kim, Dae Hyeok Lee, Seong Whan Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2281-2286
Number of pages6
ISBN (Electronic)9781665452588
DOIs
Publication statusPublished - 2022
Event2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022 - Prague, Czech Republic
Duration: 2022 Oct 92022 Oct 12

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume2022-October
ISSN (Print)1062-922X

Conference

Conference2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022
Country/TerritoryCzech Republic
CityPrague
Period22/10/922/10/12

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • 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

Fingerprint

Dive into the research topics of 'An EEGgram-based Neural Network Enhancing the Decoding Performance of Visual Imagery EEG Signals to Control the Drone Swarm'. Together they form a unique fingerprint.

Cite this