TY - GEN
T1 - VR and Exoskeleton Assisted Lower Limb Rehabilitation based on Motor Imagery BCI
AU - Su, Hao
AU - Wang, Su
AU - Huang, Mengjie
AU - Chen, Yuqing
AU - Lu, Annan
AU - Yang, Rui
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Motor imagery (MI) enhances rehabilitation in brain-injured patients by leveraging neuroplasticity to rebuild neuron connections. Combining MI with exoskeleton robotics and virtual reality (VR) can further improve recovery outcomes. In this study, we integrated a lower limb exoskeleton robot (MAX-1), the Oculus Quest 2 VR headset, and an MI-BCI system. Patients, equipped with the exoskeleton and VR headset, undertake motor imagery tasks, resulting in the exoskeleton robot's movements guided by EEG signals from the Emotiv EPOC Flex headset. These EEG signals are processed and transformed into leg commands, with concurrent feedback provided in the VR environment. The research comprises three experiments: an offline experiment for primary data collection and training, a pseudo online experiment for simulating real-time processing, and an online serious game experiment to assess the system's real-world applicability. The offline experiment showed an EEGNet model accuracy of 85.2% for MI detection and 78.3% for classification. The pseudo online experiment achieved an accuracy of 67.2%, indicative of its potential in a real-time scenario. In the online serious game experiment, while participants were able to complete the game, challenges in game control were reported, underscoring areas for future enhancement.
AB - Motor imagery (MI) enhances rehabilitation in brain-injured patients by leveraging neuroplasticity to rebuild neuron connections. Combining MI with exoskeleton robotics and virtual reality (VR) can further improve recovery outcomes. In this study, we integrated a lower limb exoskeleton robot (MAX-1), the Oculus Quest 2 VR headset, and an MI-BCI system. Patients, equipped with the exoskeleton and VR headset, undertake motor imagery tasks, resulting in the exoskeleton robot's movements guided by EEG signals from the Emotiv EPOC Flex headset. These EEG signals are processed and transformed into leg commands, with concurrent feedback provided in the VR environment. The research comprises three experiments: an offline experiment for primary data collection and training, a pseudo online experiment for simulating real-time processing, and an online serious game experiment to assess the system's real-world applicability. The offline experiment showed an EEGNet model accuracy of 85.2% for MI detection and 78.3% for classification. The pseudo online experiment achieved an accuracy of 67.2%, indicative of its potential in a real-time scenario. In the online serious game experiment, while participants were able to complete the game, challenges in game control were reported, underscoring areas for future enhancement.
KW - Brain-computer interface
KW - exoskeleton robot
KW - motor imagery
KW - serious game
KW - virtual reality
UR - http://www.scopus.com/inward/record.url?scp=85184667201&partnerID=8YFLogxK
U2 - 10.1109/IBITeC59006.2023.10390929
DO - 10.1109/IBITeC59006.2023.10390929
M3 - Conference Proceeding
AN - SCOPUS:85184667201
T3 - 2023 IEEE International Biomedical Instrumentation and Technology Conference, IBITeC 2023
SP - 74
EP - 79
BT - 2023 IEEE International Biomedical Instrumentation and Technology Conference, IBITeC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Biomedical Instrumentation and Technology Conference, IBITeC 2023
Y2 - 9 November 2023 through 10 November 2023
ER -