DocumentCode :
3579960
Title :
Complex augmentation in autonomie EEG-Cayley neural network: Integrating bipartite-trivalent graph with Erdos-Renyi in EEG network modelling
Author :
Onunka, Chiemela ; Bright, Glen ; Stopforth, Riaan
Author_Institution :
Discipline of Mech. Eng., Univ. of KwaZulu-Natal, Durban, South Africa
fYear :
2014
Firstpage :
271
Lastpage :
276
Abstract :
Cayley graph is used in representing the complex augmentation of autonomie EEG neural network with bipartite, trivalent and Erdos-Renyi models. The augmentation was used in determining an efficient communication, data and information transmission in EEG neural network. The geometric properties of EEG neural network augmented in autonomie Cayley neural network is used in the processing and transmission of EEG data. The correlation between directed communication path and optimum information transfer path ensured that EEG data and information were transmitted effortlessly to the end effector and end user. EEG network centrality revealed the geometric property of the neural network. The paper proposed the use of Cayley diagrams and graphs in the representation of autonomie EEG neural networks.
Keywords :
computational geometry; electroencephalography; end effectors; fault tolerant computing; graph theory; human-robot interaction; medical signal processing; neural nets; Cayley diagrams; Cayley graph; EEG network centrality; Erdos-Renyi model; autonomic EEG-Cayley neural network; bipartite model; communication transmission; complex augmentation representation; data transmission; directed communication path; end effector; end user; geometric properties; information transmission; optimum information transfer path; trivalent model; Biological neural networks; Brain models; Electroencephalography; Information processing; Routing; Autonomie; Bipartite; Cayley Graph; Erdos-Renyi; Trivalent;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Automation Robotics & Vision (ICARCV), 2014 13th International Conference on
Type :
conf
DOI :
10.1109/ICARCV.2014.7064317
Filename :
7064317
Link To Document :
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