Classification of brain activity using muscle activation marker for BCI controlled hand prosthesis / Khairunnisa Johar

The use of body-powered prostheses can be tiring and lead to problems with compliance and prosthetic restoration. Apparently, brain-Computer-Interfaces (BCI) offer a means of controlling prostheses for patients that are unable to operate such devices due to physical limitations. An issue with BCIs i...

Full description

Saved in:

Bibliographic Details
Main Author:	Johar, Khairunnisa
Format:	Thesis
Language:	English
Published:	2017
Subjects:	T Technology (General)
Online Access:	https://ir.uitm.edu.my/id/eprint/100233/1/100233.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uitm-ir.100233
record_format	uketd_dc
spelling	my-uitm-ir.1002332024-09-12T13:53:11Z Classification of brain activity using muscle activation marker for BCI controlled hand prosthesis / Khairunnisa Johar 2017 Johar, Khairunnisa T Technology (General) The use of body-powered prostheses can be tiring and lead to problems with compliance and prosthetic restoration. Apparently, brain-Computer-Interfaces (BCI) offer a means of controlling prostheses for patients that are unable to operate such devices due to physical limitations. An issue with BCIs is that they tend to either require invasive recording methods, posing a surgical risk or work by generating control signals from not task related brain activity patterns such as right versus left hand, hand versus leg or visual stimulation and therefore are not intuitive in their control. This study aims at testing the possibility of controlling the grasp and release of an upper limb prosthetic terminal device by classifying Electroencephalogram (EEG) data from isometric finger extension and flexion real movements. Data from five healthy subjects were recorded using a consumer grade non-invasive Emotiv EPOC headset. During the measurement of five subjects, they were asked to perform isometric finger extension and flexion of their right hand. To bring the EEG data into correlation with the executed movement, simultaneous electromyogram (EMG) recording was proposed as an alternative method for recording visual cued movement. Classified EMG data was used to generate markers in the EEG data and to the epoch data. The EEG data was then filtered to increase the signal to noise ratio and allow better classification while spectrally weighted common spatial patterns (spec-CSP) were used for feature extraction. Using linear discriminant analysis (LDA), a successful classification rate of up to 73.2% between isometric finger extension and flexion, 79.9% between isometric finger flexion and rest and 81.8% between isometric finger extension and rest were obtained. In this study, a novel EMG-assisted approach was developed for classifying EEG data from isometric finger extension and flexion movements. It has displayed feasibility for a more intuitive control on upper limb prosthetic terminal device using low-cost BCI without the risks associated with invasive measurement. 2017 Thesis https://ir.uitm.edu.my/id/eprint/100233/ https://ir.uitm.edu.my/id/eprint/100233/1/100233.pdf text en public masters Universiti Teknologi MARA (UiTM) Faculty of Mechanical Engineering
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Johar, Khairunnisa Classification of brain activity using muscle activation marker for BCI controlled hand prosthesis / Khairunnisa Johar
description	The use of body-powered prostheses can be tiring and lead to problems with compliance and prosthetic restoration. Apparently, brain-Computer-Interfaces (BCI) offer a means of controlling prostheses for patients that are unable to operate such devices due to physical limitations. An issue with BCIs is that they tend to either require invasive recording methods, posing a surgical risk or work by generating control signals from not task related brain activity patterns such as right versus left hand, hand versus leg or visual stimulation and therefore are not intuitive in their control. This study aims at testing the possibility of controlling the grasp and release of an upper limb prosthetic terminal device by classifying Electroencephalogram (EEG) data from isometric finger extension and flexion real movements. Data from five healthy subjects were recorded using a consumer grade non-invasive Emotiv EPOC headset. During the measurement of five subjects, they were asked to perform isometric finger extension and flexion of their right hand. To bring the EEG data into correlation with the executed movement, simultaneous electromyogram (EMG) recording was proposed as an alternative method for recording visual cued movement. Classified EMG data was used to generate markers in the EEG data and to the epoch data. The EEG data was then filtered to increase the signal to noise ratio and allow better classification while spectrally weighted common spatial patterns (spec-CSP) were used for feature extraction. Using linear discriminant analysis (LDA), a successful classification rate of up to 73.2% between isometric finger extension and flexion, 79.9% between isometric finger flexion and rest and 81.8% between isometric finger extension and rest were obtained. In this study, a novel EMG-assisted approach was developed for classifying EEG data from isometric finger extension and flexion movements. It has displayed feasibility for a more intuitive control on upper limb prosthetic terminal device using low-cost BCI without the risks associated with invasive measurement.
format	Thesis
qualification_level	Master's degree
author	Johar, Khairunnisa
author_facet	Johar, Khairunnisa
author_sort	Johar, Khairunnisa
title	Classification of brain activity using muscle activation marker for BCI controlled hand prosthesis / Khairunnisa Johar
title_short	Classification of brain activity using muscle activation marker for BCI controlled hand prosthesis / Khairunnisa Johar
title_full	Classification of brain activity using muscle activation marker for BCI controlled hand prosthesis / Khairunnisa Johar
title_fullStr	Classification of brain activity using muscle activation marker for BCI controlled hand prosthesis / Khairunnisa Johar
title_full_unstemmed	Classification of brain activity using muscle activation marker for BCI controlled hand prosthesis / Khairunnisa Johar
title_sort	classification of brain activity using muscle activation marker for bci controlled hand prosthesis / khairunnisa johar
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Mechanical Engineering
publishDate	2017
url	https://ir.uitm.edu.my/id/eprint/100233/1/100233.pdf
_version_	1811769069537329152

Classification of brain activity using muscle activation marker for BCI controlled hand prosthesis / Khairunnisa Johar

Similar Items