EEG artefact identification and extraction in autonomic wireless network for future coordination and control of semi-autonomous systems.
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Date
2015
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Abstract
Electroencephalographic signals is used to show correlations between specific forms of cognitive
activities and robotic hand motion. This research presents EEG artefact identification, extraction and
classification for use in the development of a robotic hand. The findings from the study were used to
control a robotic arm and develop a suitable communication network that has no dependence on the
human nervous system communication pathways.
The research was focused at modelling bio-sensing and bio-monitoring feedback system using
electroencephalographic (EEG) as the source signal. An EEG communication system was developed
for implementation on the robotic hand developed by the Mechatronics and Robotics Research Group
(MR2G). Neuronal activities produce electrical signals on surface of scalp in human beings. EEG the
raw material for robot command development was generated from the neuronal activities. Specific
techniques were used in modelling the EEG analysis system for implementation on the robotic hand.
The techniques used include the Radial Basis Function (RBF) neural network, Linear Discriminant
Analysis (LDA), Principal Component Analysis (PCA), Singular Value Decomposition (SVD),
Wavelet Packet Transform (WPT), Multilayer Perceptron Neural Network (MLPNN), Learning Vector
Quantization (LVQ) neural network, Bayesian and probabilistic paradigms in developing the EEG
artefact identification, extraction and classification model. These techniques were investigated and
implemented in order to have an efficient EEG artefact identification and extraction system for
controlling the robotic hand.
The main contribution of the research was the identification, extraction and classification of
electroencephalographic (EEG) artefacts in controlling a robotic hand. The specific contribution made
in the research included the development of augmented EEG signal and EEG artefact extraction process
using mathematical models. The models were used to develop integrated coordination and control
architecture for the robotic arm. The research also made significant contribution to the development of
modular Brain-Computer Interface (BCI) communication network. The BCI was augmented in
autonomic wireless neural network activated by various EEG artefacts. The robotic hand control
command codes were developed and they were modular in their application strategy. This was
consolidated with adequate software and hardware architecture which were reconfigurable and
leveraged using neuro-symbolic behaviour language in controlling the robotic hand developed by the
Mechatronic and Robotic Research Group (MR2G).
Description
Ph. D. University of KwaZulu-Natal, Durban 2015.
Keywords
Electroencephalography., Robotics -- Human factors., Autonomous robots., Theses -- Mechanical engineering.