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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| Format: | Thesis |
| Language: | English |
| Subjects: | |
| 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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| Summary: | 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. |
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