EEG-based neural decoding of gait in developing children

Trieu Phat Luu; David Eguren; Manuel Cestari; Jose L. Contreras-Vidal

doi:10.1109/SMC.2019.8914380

EEG-based neural decoding of gait in developing children

Trieu Phat Luu, David Eguren, Manuel Cestari, Jose L. Contreras-Vidal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3608-3612
Number of pages	5
ISBN (Electronic)	9781728145693
DOIs	https://doi.org/10.1109/SMC.2019.8914380
State	Published - Oct 2019
Event	2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019 - Bari, Italy Duration: Oct 6 2019 → Oct 9 2019

Publication series

Name	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume	2019-October
ISSN (Print)	1062-922X

Other

Other	2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
Country/Territory	Italy
City	Bari
Period	10/6/19 → 10/9/19

Keywords

Brain-computer interface
Children walking
EEG
EMG
Gait
Neural decoding

ASJC Scopus subject areas

Electrical and Electronic Engineering
Control and Systems Engineering
Human-Computer Interaction

Access to Document

10.1109/SMC.2019.8914380

Cite this

Luu, T. P., Eguren, D., Cestari, M., & Contreras-Vidal, J. L. (2019). EEG-based neural decoding of gait in developing children. In 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019 (pp. 3608-3612). Article 8914380 (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics; Vol. 2019-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2019.8914380

EEG-based neural decoding of gait in developing children. / Luu, Trieu Phat; Eguren, David; Cestari, Manuel et al.
2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 3608-3612 8914380 (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics; Vol. 2019-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Luu, TP, Eguren, D, Cestari, M & Contreras-Vidal, JL 2019, EEG-based neural decoding of gait in developing children. in 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019., 8914380, Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, vol. 2019-October, Institute of Electrical and Electronics Engineers Inc., pp. 3608-3612, 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019, Bari, Italy, 10/6/19. https://doi.org/10.1109/SMC.2019.8914380

Luu TP, Eguren D, Cestari M, Contreras-Vidal JL. EEG-based neural decoding of gait in developing children. In 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 3608-3612. 8914380. (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics). doi: 10.1109/SMC.2019.8914380

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abstract = "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.",

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note = "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: tpluu2207@gmail.com). 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: {\textcopyright} 2019 IEEE. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019 ; Conference date: 06-10-2019 Through 09-10-2019",

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PY - 2019/10

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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.

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EEG-based neural decoding of gait in developing children

Abstract

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Keywords

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