A Noninvasive Neural Interface for Control of a Powered Lower Limb Prosthesis

Simple item page
dc.contributor.advisorContreras-Vidal, Jose L.
dc.contributor.committeeMemberPrasad, Saurabh
dc.contributor.committeeMemberFaghih, Rose T.
dc.contributor.committeeMemberZhang, Yingchun
dc.contributor.committeeMemberYau, Jeffery Min-In
dc.contributor.committeeMemberHowell, Jared
dc.creatorBrantley, Justin Alexander
dc.creator.orcid0000-0001-8854-6398
dc.date.accessioned2020-01-03T05:55:40Z
dc.date.createdDecember 2019
dc.date.issued2019-12
dc.date.submittedDecember 2019
dc.date.updated2020-01-03T05:55:41Z
dc.description.abstractLimb amputation results in a physical disability that causes activities of daily living to become difficult or impossible for the amputee. Current lower-limb prostheses provide limited control and result in only modest improvements in mobility for the amputee. Recent advancements in powered lower limb prostheses allow for more intelligent control and better walking functions. The incorporation of neural signals, specifically muscle and brain, may offer a viable method for improved volitional control by directly interpreting signals from muscle and cortical brain activations. In this study, we employ a multimodal neuroimaging approach to determine if noninvasively recorded brain signals can be used within a lower limb prosthesis control scheme. First, we use a mobile brain/body imaging (MoBI) approach to identify the neural correlates of walking during terrain transitions between level ground and stair ascent. These data are then used to demonstrate the feasibility of predicting the two terrains directly from EEG signals, with cross-validation accuracies achieving greater than 80% in offline decoding. Second, we utilize functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) to study the brain of amputees during isolated movements of the intact and phantom limb. These data are used to: (1) identify neural correlates of movement during isolated limb movements in the amputee population, and (2) demonstrate the feasibility of control of a powered lower limb prosthesis using neural signals from the brain and muscles. We observed that the representation of the phantom limb is preserved in the deprived cortex. Additionally, using a Kalman Filtering approach, we achieved moderate reconstruction accuracy for predicting movements of the phantom and intact limb directly from EEG. The work in this dissertation contributes to a greater understanding of the neural signals associated with phantom limb movements in lower limb amputees and presents a strategy for neural control of powered prostheses.
dc.description.departmentElectrical and Computer Engineering, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.citationPortions of this document appear in: Brantley, Justin A., Trieu Phat Luu, Sho Nakagome, Fangshi Zhu, and Jose L. Contreras-Vidal. "Full body mobile brain-body imaging data during unconstrained locomotion on stairs, ramps, and level ground." Scientific data 5 (2018): 180133.
dc.identifier.urihttps://hdl.handle.net/10657/5653
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectEEG
dc.subjectBrain-machine interface
dc.subjectAmputee
dc.subjectLower limb prosthesis
dc.titleA Noninvasive Neural Interface for Control of a Powered Lower Limb Prosthesis
dc.type.dcmiText
dc.type.genreThesis
local.embargo.lift2021-12-01
local.embargo.terms2021-12-01
thesis.degree.collegeCullen College of Engineering
thesis.degree.departmentElectrical and Computer Engineering, Department of
thesis.degree.disciplineElectrical Engineering
thesis.degree.grantorUniversity of Houston
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
BRANTLEY-DISSERTATION-2019.pdf
Size:
36.64 MB
Format:
Adobe Portable Document Format
Download

License bundle

Now showing 1 - 2 of 2
No Thumbnail Available
Name:
PROQUEST_LICENSE.txt
Size:
4.43 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
LICENSE.txt
Size:
1.82 KB
Format:
Plain Text
Description:
Download

Collections

Published ETD Collection