Classification of meaningful and meaningless visual objects: a graph similarity approach

Cognition involves dynamic reconfiguration of functional brain networks at sub-second time scale. A precise tracking of these reconfigurations to categorize visual objects remains elusive. Here, we use dense electroencephalography (EEG) data recorded during naming meaningful (tools, animals) and scr...

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Bibliographic Details
Published inarXiv.org
Main Authors Mheich, Ahmad, Hassan, Mahmoud, Wendling, Fabrice
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 02.06.2017
Subjects
Algorithms
Brain
Cognition
Data processing
Electroencephalography
Networks
Reaction time
Reconfiguration
Similarity
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Summary:Cognition involves dynamic reconfiguration of functional brain networks at sub-second time scale. A precise tracking of these reconfigurations to categorize visual objects remains elusive. Here, we use dense electroencephalography (EEG) data recorded during naming meaningful (tools, animals) and scrambled objects from 20 healthy subjects. We combine technique for identifying functional brain networks and recently developed algorithm for estimating networks similarity to discriminate between the two categories. First, we showed that dynamic networks of both categories can be segmented into several brain network states (times windows with consistent brain networks) reflecting sequential information processing from object representation to reaction time. Second, using a network similarity algorithm, results showed high intra-category and very low inter-category values. An average accuracy of 76% was obtained at different brain network states.
ISSN:2331-8422