TY - GEN
T1 - Mind Controlled Vehicle Based on Lidar SLAM Navigation and SSVEP Technology
AU - Liu, Siyu
AU - Zhang, Deyu
AU - Qiao, Min
AU - Wang, Kai
AU - Zhao, Siteng
AU - Yang, Yuxuan
AU - Yan, Tianyi
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/2/22
Y1 - 2021/2/22
N2 - In recent years, Rapid development had been made by brain computer interfaces(BCI) technology, providing a new way of communication between people and the outside world. Researchers proposed varied BCI based methods to control Robotic arms, exoskeletons, and robotic cars for locomotion. However, most of the contemporary BCI systems were poor in human-computer interaction and user experience. For example, in mind-controlled vehicles, controllers would not know the location of the vehicle in real time scene. On the other hand, traditional BCI, especially visual stimulated(VS) BCI methods lacked efficiency, for most VS-BCIs were driven by selective binary commands such as moving forward or backward. To solve this problem, we first proposed a hybrid BCI strategy for mind controlled vehicle, which involved simultaneous localization/mapping(SLAM) and steady state visual evoked potential(SSVEP), by which users could achieve mind control of a vehicle equipped with lidar. We designed a BCI system based on SLAM-SSVEP paradigm and carried out online experimental verification. The experimental results showed that all participants in the online experiment could achieve effective control of the BCI system based on SLAM-SSVEP paradigm.
AB - In recent years, Rapid development had been made by brain computer interfaces(BCI) technology, providing a new way of communication between people and the outside world. Researchers proposed varied BCI based methods to control Robotic arms, exoskeletons, and robotic cars for locomotion. However, most of the contemporary BCI systems were poor in human-computer interaction and user experience. For example, in mind-controlled vehicles, controllers would not know the location of the vehicle in real time scene. On the other hand, traditional BCI, especially visual stimulated(VS) BCI methods lacked efficiency, for most VS-BCIs were driven by selective binary commands such as moving forward or backward. To solve this problem, we first proposed a hybrid BCI strategy for mind controlled vehicle, which involved simultaneous localization/mapping(SLAM) and steady state visual evoked potential(SSVEP), by which users could achieve mind control of a vehicle equipped with lidar. We designed a BCI system based on SLAM-SSVEP paradigm and carried out online experimental verification. The experimental results showed that all participants in the online experiment could achieve effective control of the BCI system based on SLAM-SSVEP paradigm.
KW - FBCCA
KW - SLAM
KW - SSVEP
KW - brain-computer interface
UR - http://www.scopus.com/inward/record.url?scp=85104893968&partnerID=8YFLogxK
U2 - 10.1109/BCI51272.2021.9385312
DO - 10.1109/BCI51272.2021.9385312
M3 - Conference contribution
AN - SCOPUS:85104893968
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 -