@inproceedings{4f0f632cc8e74bc680e6bd6ca8f3b729,
title = "EEG-based neural decoding of gait in developing children",
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.",
keywords = "Brain-computer interface, Children walking, EEG, EMG, Gait, Neural decoding",
author = "Luu, {Trieu Phat} and David Eguren and Manuel Cestari and Contreras-Vidal, {Jose L.}",
year = "2019",
month = oct,
doi = "10.1109/SMC.2019.8914380",
language = "English (US)",
series = "Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3608--3612",
booktitle = "2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019",
address = "United States",
note = "2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019 ; Conference date: 06-10-2019 Through 09-10-2019",
}