Prediction of EMG envelopes of multiple terrains over-ground walking from EEG signals using an Unscented Kalman Filter

Sho Nakagome, Trieu Phat Luu, Justin A. Brantley, Jose L. Contreras-Vidal

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

4 Scopus citations

Abstract

Advanced powered lower-limb prosthetic devices require an intuitive and flexible user control interface to work in a dynamic environment. This study investigated the feasibility of inferring muscle activation patterns (electromyography, EMG, envelope) from non-invasive electroencephalography (EEG) signals. Six healthy individuals participated in this study; the subjects were instructed to walk at a comfortable speed across various terrains (e.g. level-ground, up/down slope, up/down stair walking). An unscented kalman filter (UKF) was used to predict the EMG envelope from fluctuations in the amplitude of slow cortical potentials of EEG in the delta band (0.1-3 Hz). The highest decoding accuracy obtained was an r-value (Pearson's correlation r-value) of 0.57 in the medial gastrocnemius of a single subject. In the same subject, the mean r-value across all the muscle groups exceeded 0.4. The mean accuracy across all subjects and muscle group corresponded to an r-value of 0.236. As for the Signal to Noise Ratio (SNR), 79.3% of the obtained results were more than 0 dB with mean performance SNR of 0.8 (max: 2.8 to min: -1.7). The highest accuracy was obtained using a lag of 50ms with a window length (tap) of 500ms. In conclusion, this is the first study to show offline continuous decoding of the EMG envelope during over-ground walking on multiple terrains. The results show the feasibility of such neural decoding. This method could be coupled with EMG-based terrain prediction techniques to further improve the neural control interface with powered lower-limb prostheses.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3175-3178
Number of pages4
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

  • Electroencephalography (EEG)
  • Electromyography (EMG)
  • Over ground walking
  • Unscented Kalman Filter (UKF)

ASJC Scopus subject areas

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

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