TY - GEN
T1 - EEG-based neural decoding of gait in developing children
AU - Luu, Trieu Phat
AU - Eguren, David
AU - Cestari, Manuel
AU - Contreras-Vidal, Jose L.
N1 - Funding Information:
This research was supported by NSF IUCRC BRAIN, Mission Connect – A TIRR Foundation, the University of Houston Cullen College of Engineering, and NSF Awards CNS 1650536 T.P. Luu, D. Eguren, M. Cestari, and J.L. Contreras-Vidal, are with the NSF IUCRC BRAIN Center at the University of Houston, Houston, TX 77204 USA, e-mail: [email protected]).
Funding Information:
This research was supported by NSF IUCRC BRAIN, Mission Connect ? A TIRR Foundation, the University of Houston Cullen College of Engineering, and NSF Awards CNS 1650536
Publisher Copyright:
© 2019 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/10
Y1 - 2019/10
N2 - Neural decoding of human locomotion, including automated gait intention detection and continuous decoding of lower limb joint angles, has been of great interest in the field of Brain Machine Interface (BMI). However, neural decoding of gait in developing children has yet to be demonstrated. In this study, we collected physiological data (electroencephalography (EEG), electromyography (EMG)), and kinematic data from children performing different locomotion tasks. We also developed a state space estimation model to decode lower limb joint angles from scalp EEG. Fluctuations in the amplitude of slow cortical potentials of EEG in the delta band (0.1-3 Hz) were used for prediction. The decoding accuracies (Pearson's r values) were promising (Hip: 0.71; Knee: 0.59; Ankle: 0.51). Our results demonstrate the feasibility of neural decoding of children walking and have implications for the development of a real-time closed-loop BMI system for the control of a pediatric exoskeleton.
AB - Neural decoding of human locomotion, including automated gait intention detection and continuous decoding of lower limb joint angles, has been of great interest in the field of Brain Machine Interface (BMI). However, neural decoding of gait in developing children has yet to be demonstrated. In this study, we collected physiological data (electroencephalography (EEG), electromyography (EMG)), and kinematic data from children performing different locomotion tasks. We also developed a state space estimation model to decode lower limb joint angles from scalp EEG. Fluctuations in the amplitude of slow cortical potentials of EEG in the delta band (0.1-3 Hz) were used for prediction. The decoding accuracies (Pearson's r values) were promising (Hip: 0.71; Knee: 0.59; Ankle: 0.51). Our results demonstrate the feasibility of neural decoding of children walking and have implications for the development of a real-time closed-loop BMI system for the control of a pediatric exoskeleton.
KW - Brain-computer interface
KW - Children walking
KW - EEG
KW - EMG
KW - Gait
KW - Neural decoding
UR - http://www.scopus.com/inward/record.url?scp=85076768683&partnerID=8YFLogxK
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U2 - 10.1109/SMC.2019.8914380
DO - 10.1109/SMC.2019.8914380
M3 - Conference contribution
AN - SCOPUS:85076768683
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 3608
EP - 3612
BT - 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
Y2 - 6 October 2019 through 9 October 2019
ER -