Cortical features of locomotion-mode transitions via non-invasive EEG

Trieu Phat Luu, Justin A. Brantley, Fangshi Zhu, Jose L. Contreras-Vidal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2437-2441
Number of pages5
ISBN (Electronic)9781538616451
DOIs
StatePublished - Nov 27 2017
Event2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017 - Banff, Canada
Duration: Oct 5 2017Oct 8 2017

Publication series

Name2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
Volume2017-January

Other

Other2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
CountryCanada
CityBanff
Period10/5/1710/8/17

Keywords

  • Brain Machine Interface
  • Electroencephalography (EEG)
  • Electromyography (EMG)
  • Neural interfaces
  • Prosthetic legs
  • User intent recognition

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Control and Optimization

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