Functional Connectivity Analysis of Motor Imagery EEG Signal for Brain-Computer Interfacing Application

Poulami Ghosh 1 Ankita Mazumder 1 Saugat Bhattacharyya 1 Dewaki Nanddan Tibarewala 1 Mitsuhiro Hayashibe 2
2 DEMAR - Artificial movement and gait restoration
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : The human brain can be considered as a graphical network having different regions with specific functionality and it can be said that a virtual functional connectivity are present between these regions. These regions are regarded as nodes and the functional links are regarded as the edges between them. The intensity of these functional links depend on the activation of the lobes while performing a specific task(e.g. motor imagery tasks, cognitive tasks and likewise). The main aim of this study is to understand the activation of the parts of the brain while performing three types of motor imagery tasks with the help of graph theory. Two indices of the graph, namely Network Density and Node Strength are calculated for 32 electrodes placed on the subject's head covering all the brain lobes and the nodes having higher intensity are identified.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01235857
Contributor : Mitsuhiro Hayashibe <>
Submitted on : Monday, November 30, 2015 - 6:33:58 PM
Last modification on : Tuesday, June 25, 2019 - 2:16:43 PM

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Poulami Ghosh, Ankita Mazumder, Saugat Bhattacharyya, Dewaki Nanddan Tibarewala, Mitsuhiro Hayashibe. Functional Connectivity Analysis of Motor Imagery EEG Signal for Brain-Computer Interfacing Application. NER: Neural Engineering, Apr 2015, Montpellier, France. pp.210-213, ⟨10.1109/NER.2015.7146597⟩. ⟨lirmm-01235857⟩

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