• 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