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Classification of gait direction using brain signals

Resource type
Thesis type
(Thesis) M.A.Sc.
Date created
2022-08-25
Authors/Contributors
Abstract
The use of brain-computer interface (BCI) technology has emerged as a promising rehabilitation approach for patients with motor function and motor-related disorders. Brain recordings collected by an electroencephalography (EEG) system have been widely employed in BCI platforms including assistive robots such as exoskeletons. Despite the large body of literature on upper-limb exoskeleton, few studies were dedicated to lower-limb exoskeletons. The current literature is limited to decoding the EEG rhythms to identify gait intention and gait variations to ultimately command a lower-limb robotic exoskeleton. This experimental study, however, aims to decode the EEG signals for the actions of gait toward four different directions and stop. Three different deep neural network paradigms were used to generate distinct classification models. Ten-fold cross-validation scheme was employed to compare the classification performance obtained for each of these models. The outcome of this study has the potential to be ultimately used in lower-limb exoskeletons for interactive navigation in robotic rehabilitation therapy.
Document
Extent
88 pages.
Identifier
etd22145
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Park, Edward J.
Thesis advisor: Arzanpour, Siamak
Language
English
Download file Size
etd22145.pdf 2.45 MB

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