TY - GEN
T1 - Cortical features of locomotion-mode transitions via non-invasive EEG
AU - Luu, Trieu Phat
AU - Brantley, Justin A.
AU - Zhu, Fangshi
AU - Contreras-Vidal, Jose L.
N1 - Funding Information:
The authors would like to thank Fangshi Zhu and Dr. Recep Ozdemir for their efforts in data collection. We would like to thank the Center for Neuromotor and Biomechanics Research (CNBR) for sharing the MVN Xsens system for data collection.
Funding Information:
This research is partly supported by NSF awards IIS-1302339.
Publisher Copyright:
© 2017 IEEE.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2017/11/27
Y1 - 2017/11/27
N2 - This study investigates the neural features of locomotion mode transitions (i.e., level-ground walking to stair ascent) from non-invasive electroencephalography (EEG) signals. A systematic EEG processing method was implemented to reduce artifacts. Source localization using independent component analysis and k-mean clustering algorithm revealed the involvement of four clusters in the brain (Left and Right Occipital Lobe, Posterior Parietal Cortex, and Motor Cortex) during the walking tasks. Our results show significant differences in spectral power in the Occipital cluster between level-ground (LW) and stair (SA) walking. Additionally, significant increases in spectral power were detected up to 1.4 second before the critical transition time (LW to SA). The findings have implications for developing noninvasive lower-limb neuroprostheses that predict, rather than respond to, the user gait intentions. This work is a further step toward the development of a multimodal Neural-machine Interface (NMI) that fuses EEG and electromyography (EMG) signals for intuitive and flexible control of power prosthetic legs.
AB - This study investigates the neural features of locomotion mode transitions (i.e., level-ground walking to stair ascent) from non-invasive electroencephalography (EEG) signals. A systematic EEG processing method was implemented to reduce artifacts. Source localization using independent component analysis and k-mean clustering algorithm revealed the involvement of four clusters in the brain (Left and Right Occipital Lobe, Posterior Parietal Cortex, and Motor Cortex) during the walking tasks. Our results show significant differences in spectral power in the Occipital cluster between level-ground (LW) and stair (SA) walking. Additionally, significant increases in spectral power were detected up to 1.4 second before the critical transition time (LW to SA). The findings have implications for developing noninvasive lower-limb neuroprostheses that predict, rather than respond to, the user gait intentions. This work is a further step toward the development of a multimodal Neural-machine Interface (NMI) that fuses EEG and electromyography (EMG) signals for intuitive and flexible control of power prosthetic legs.
KW - Brain Machine Interface
KW - Electroencephalography (EEG)
KW - Electromyography (EMG)
KW - Neural interfaces
KW - Prosthetic legs
KW - User intent recognition
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U2 - 10.1109/SMC.2017.8122988
DO - 10.1109/SMC.2017.8122988
M3 - Conference contribution
AN - SCOPUS:85044362262
T3 - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
SP - 2437
EP - 2441
BT - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
Y2 - 5 October 2017 through 8 October 2017
ER -