TY - GEN
T1 - Design of an EEG-based Drone Swarm Control System using Endogenous BCI Paradigms
AU - Lee, Dae Hyeok
AU - Jeong, Ji Hoon
AU - Ahn, Hyung Ju
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 - 2021/2/22
Y1 - 2021/2/22
N2 - Non-invasive brain-computer interface (BCI) has been developed for understanding users' intentions by using electroencephalogram (EEG) signals. With the recent development of artificial intelligence, there have been many developments in the drone control system. BCI characteristic that can reflect the users' intentions led to the BCI-based drone control system. When using drone swarm, we can have more advantages, such as mission diversity, than using a single drone. In particular, BCI-based drone swarm control could provide many advantages to various industries such as military service or industry disaster. BCI Paradigms consist of the exogenous and endogenous paradigms. The endogenous paradigms can operate with the users' intentions independently of any stimulus. In this study, we designed endogenous paradigms (i.e., motor imagery (MI), visual imagery (VI), and speech imagery (SI)) specialized in drone swarm control, and EEG-based various task classifications related to drone swarm control were conducted. Five subjects participated in the experiment and the performance was evaluated using the basic machine learning algorithm. The grand-averaged accuracies were 37.6% (± 6.78), 43.2% (± 3.44), and 31.6% (± 1.07) in MI, VI, and SI, respectively. Hence, we confirmed the feasibility of increasing the degree of freedom for drone swarm control using various endogenous paradigms.
AB - Non-invasive brain-computer interface (BCI) has been developed for understanding users' intentions by using electroencephalogram (EEG) signals. With the recent development of artificial intelligence, there have been many developments in the drone control system. BCI characteristic that can reflect the users' intentions led to the BCI-based drone control system. When using drone swarm, we can have more advantages, such as mission diversity, than using a single drone. In particular, BCI-based drone swarm control could provide many advantages to various industries such as military service or industry disaster. BCI Paradigms consist of the exogenous and endogenous paradigms. The endogenous paradigms can operate with the users' intentions independently of any stimulus. In this study, we designed endogenous paradigms (i.e., motor imagery (MI), visual imagery (VI), and speech imagery (SI)) specialized in drone swarm control, and EEG-based various task classifications related to drone swarm control were conducted. Five subjects participated in the experiment and the performance was evaluated using the basic machine learning algorithm. The grand-averaged accuracies were 37.6% (± 6.78), 43.2% (± 3.44), and 31.6% (± 1.07) in MI, VI, and SI, respectively. Hence, we confirmed the feasibility of increasing the degree of freedom for drone swarm control using various endogenous paradigms.
KW - brain-computer interface
KW - drone swarm control
KW - electroencephalogram
KW - intuitive paradigm
UR - http://www.scopus.com/inward/record.url?scp=85104821925&partnerID=8YFLogxK
U2 - 10.1109/BCI51272.2021.9385356
DO - 10.1109/BCI51272.2021.9385356
M3 - Conference contribution
AN - SCOPUS:85104821925
T3 - 9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021
BT - 9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021
Y2 - 22 February 2021 through 24 February 2021
ER -