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Title: EEG-based Neural Decoding of Gait in Developing Children
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.  more » « less
Award ID(s):
1650536
PAR ID:
10129940
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)
Page Range / eLocation ID:
3608 to 3612
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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