NDLI: Graph theory in brain-to-brain connectivity: A simulation study and an application to an EEG hyperscanning experiment

Content Provider	IEEE Xplore Digital Library
Author	Toppi, J. Ciaramidaro, A. Vogel, P. Mattia, D. Babiloni, F. Siniatchkin, M. Astolfi, L.
Copyright Year	2015
Description	Author affiliation: Dept. of Comput., Control, & Manage. Eng., Univ. of Rome “Sapienza”, Rome, Italy (Toppi, J.; Astolfi, L.) \|\| Inst. of Med. Psychol. & Med. Sociology, Univ. of Kiel, Kiel, Germany (Siniatchkin, M.) \|\| Dept. of Physiol. & Pharmacology, Univ. of Rome “Sapienza”, Rome, Italy (Babiloni, F.) \|\| IRCCS Fondazione Santa Lucia, Rome, Italy (Mattia, D.) \|\| Neurosci. Center, Goethe-Univ. Frankfurt, Frankfurt, Germany (Vogel, P.) \|\| Dept. of Child & Adolescent Psychiatry, Johann Wolfgang Goethe Univ., Frankfurt am Main, Germany (Ciaramidaro, A.)
Abstract	Hyperscanning consists in the simultaneous recording of hemodynamic or neuroelectrical signals from two or more subjects acting in a social context. Well-established methodologies for connectivity estimation have already been adapted to hyperscanning purposes. The extension of graph theory approach to multi-subjects case is still a challenging issue. In the present work we aim to test the ability of the currently used graph theory global indices in describing the properties of a network given by two interacting subjects. The testing was conducted first on surrogate brain-to-brain networks reproducing typical social scenarios and then on real EEG hyperscanning data recorded during a Joint Action task. The results of the simulation study highlighted the ability of all the investigated indexes in modulating their values according to the level of interaction between subjects. However, only global efficiency and path length indexes demonstrated to be sensitive to an asymmetry in the communication between the two subjects. Such results were, then, confirmed by the application on real EEG data. Global efficiency modulated, in fact, their values according to the inter-brain density, assuming higher values in the social condition with respect to the non-social condition.
Starting Page	2211
Ending Page	2214
File Size	853018
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7318830
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Indexes Electroencephalography Joints Brain modeling Graph theory Analysis of variance Neuroscience
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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