Brain Machine Interface Controlled Robot Chair

Brain Machine Interface Controlled Robot Chair: Brain Machine Interface is a device that links the human brain directly to devices such as computer, wheelchairs and prosthetic arms. Such interfaces provide a digital channel for communication and control in the absence of the biological channels a...

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Main Author:	Hema Chengalvarayan, Radhakrishnamurthy
Format:	Thesis
Language:	English
Subjects:	Controlled robot chair Brain machine interface (BMI) Electroencephalography (EEG) Wheel chair
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/9860/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/9860/2/Full%20Text.pdf
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id	my-unimap-9860
record_format	uketd_dc
spelling	my-unimap-98602019-09-26T06:26:45Z Brain Machine Interface Controlled Robot Chair Hema Chengalvarayan, Radhakrishnamurthy Brain Machine Interface Controlled Robot Chair: Brain Machine Interface is a device that links the human brain directly to devices such as computer, wheelchairs and prosthetic arms. Such interfaces provide a digital channel for communication and control in the absence of the biological channels and thus help in the rehabilitation of mobility and speech impaired individuals. In this thesis, a novel four-class brain machine interface (BMI) is designed for a robot chair using neural networks. Simple and novel protocols for acquiring brain EEG signals from two non-invasive scalp electrodes are presented. Four tasks based on motor imagery of left and right hand movements are proposed to control the directions of the robot chair. A novel algorithm for acquisition of motor imagery signals using only hand movements is proposed. Simple preprocessing algorithms are presented to remove noise from the raw signals. Mu, Beta and Gamma frequency bands related to the motor actions are extracted using customised filters. New features based on time and frequency components of the EEG signals are proposed and tested with classifiers. Classification of the four hand motor imagery signals is presented using static and dynamic neural networks. A particle swarm optimization based algorithm is proposed to train the neural networks. Combinations of the features proposed and the static and dynamic classifiers are analysed. Signals collected from 10 trained subjects are used in the analysis of synchronous and asynchronous BMI designs. A max-one algorithm for translation of the hand motor imagery signals into robot chair movements is presented. A prototype robot chair is designed and interfaced with the developed asynchronous BMI. Safety features are integrated through a collision avoidance system to enhance the performance of the robot chair. The BMI controls the joystick of the robot chair using a shared control algorithm. Real-time experiments are also presented using 10 trained and 5 untrained subjects to validate the applicability of the brain machine interface. Experiments were carried out at two expositions (out-of-lab environments) with 25 untrained subjects to assess its feasibility in real life environments. Universiti Malaysia Perlis (UniMAP) 2010 Thesis en http://dspace.unimap.edu.my/123456789/9860 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/9860/3/license.txt 924dcad4b739ede91b3c2049af5d954b http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/9860/1/Page%201-24.pdf 891271f2c294074c4e5a7bb7f2372909 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/9860/2/Full%20Text.pdf 777a839bfeea2e533412872fdd9a68ad Controlled robot chair Brain machine interface (BMI) Electroencephalography (EEG) Wheel chair School of Mechatronic Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
topic	Controlled robot chair Brain machine interface (BMI) Electroencephalography (EEG) Wheel chair
spellingShingle	Controlled robot chair Brain machine interface (BMI) Electroencephalography (EEG) Wheel chair Hema Chengalvarayan, Radhakrishnamurthy Brain Machine Interface Controlled Robot Chair
description	Brain Machine Interface Controlled Robot Chair: Brain Machine Interface is a device that links the human brain directly to devices such as computer, wheelchairs and prosthetic arms. Such interfaces provide a digital channel for communication and control in the absence of the biological channels and thus help in the rehabilitation of mobility and speech impaired individuals. In this thesis, a novel four-class brain machine interface (BMI) is designed for a robot chair using neural networks. Simple and novel protocols for acquiring brain EEG signals from two non-invasive scalp electrodes are presented. Four tasks based on motor imagery of left and right hand movements are proposed to control the directions of the robot chair. A novel algorithm for acquisition of motor imagery signals using only hand movements is proposed. Simple preprocessing algorithms are presented to remove noise from the raw signals. Mu, Beta and Gamma frequency bands related to the motor actions are extracted using customised filters. New features based on time and frequency components of the EEG signals are proposed and tested with classifiers. Classification of the four hand motor imagery signals is presented using static and dynamic neural networks. A particle swarm optimization based algorithm is proposed to train the neural networks. Combinations of the features proposed and the static and dynamic classifiers are analysed. Signals collected from 10 trained subjects are used in the analysis of synchronous and asynchronous BMI designs. A max-one algorithm for translation of the hand motor imagery signals into robot chair movements is presented. A prototype robot chair is designed and interfaced with the developed asynchronous BMI. Safety features are integrated through a collision avoidance system to enhance the performance of the robot chair. The BMI controls the joystick of the robot chair using a shared control algorithm. Real-time experiments are also presented using 10 trained and 5 untrained subjects to validate the applicability of the brain machine interface. Experiments were carried out at two expositions (out-of-lab environments) with 25 untrained subjects to assess its feasibility in real life environments.
format	Thesis
author	Hema Chengalvarayan, Radhakrishnamurthy
author_facet	Hema Chengalvarayan, Radhakrishnamurthy
author_sort	Hema Chengalvarayan, Radhakrishnamurthy
title	Brain Machine Interface Controlled Robot Chair
title_short	Brain Machine Interface Controlled Robot Chair
title_full	Brain Machine Interface Controlled Robot Chair
title_fullStr	Brain Machine Interface Controlled Robot Chair
title_full_unstemmed	Brain Machine Interface Controlled Robot Chair
title_sort	brain machine interface controlled robot chair
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Mechatronic Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/9860/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/9860/2/Full%20Text.pdf
_version_	1747836779785879552

Brain Machine Interface Controlled Robot Chair

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