EEG artefact identification and extraction in autonomic wireless network for future coordination and control of semi-autonomous systems.

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).