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Using brain connectivity measure of EEG synchrostates for discriminating typical and autism spectrum disorder

Using brain connectivity measure of EEG synchrostates for discriminating typical and autism spectrum disorder
Using brain connectivity measure of EEG synchrostates for discriminating typical and autism spectrum disorder
In this paper we utilized the concept of stable phase synchronization topography – synchrostates – over the scalp derived from EEG recording for formulating brain connectivity network in Autism Spectrum Disorder (ASD) and typically-growing children. A synchronization index is adapted for forming the edges of the connectivity graph capturing the stability of each of the synchrostates. Such network is formed for 11 ASD and 12 control group children. Comparative analyses of these networks using graph theoretic measures show that children with autism have a different modularity of such networks from typical children. This result could pave the way to a new modality for possible identification of ASD from non-invasively recorded EEG data
1402-1405
Jamal, Wasifa
3f70176e-843e-46b7-8447-4eefaef104f1
Das, Saptarshi
e06f2eb0-1e3e-453c-ba78-82eed18ceac9
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Kuyucu, Doga
7798a520-38f0-43ab-8854-a8643bd5558c
Sicca, Federico
eca600aa-5535-4bff-b5ac-9b82ff3fe034
Billeci, Lucia
0833bdc3-fbd3-42ec-8114-268223be5857
Apicella, Fabio
dad56776-dd88-41ab-a190-58537d29ab32
Muratori, Filippo
72a8f12f-671c-40e6-b3bc-219d497b6d76
Jamal, Wasifa
3f70176e-843e-46b7-8447-4eefaef104f1
Das, Saptarshi
e06f2eb0-1e3e-453c-ba78-82eed18ceac9
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Kuyucu, Doga
7798a520-38f0-43ab-8854-a8643bd5558c
Sicca, Federico
eca600aa-5535-4bff-b5ac-9b82ff3fe034
Billeci, Lucia
0833bdc3-fbd3-42ec-8114-268223be5857
Apicella, Fabio
dad56776-dd88-41ab-a190-58537d29ab32
Muratori, Filippo
72a8f12f-671c-40e6-b3bc-219d497b6d76

Jamal, Wasifa, Das, Saptarshi and Maharatna, Koushik et al. (2013) Using brain connectivity measure of EEG synchrostates for discriminating typical and autism spectrum disorder. Neural Engineering (NER), 2013 6th International IEEE/EMBS Conference on, United States. 06 - 08 Nov 2013. pp. 1402-1405 . (doi:10.1109/NER.2013.6696205).

Record type: Conference or Workshop Item (Paper)

Abstract

In this paper we utilized the concept of stable phase synchronization topography – synchrostates – over the scalp derived from EEG recording for formulating brain connectivity network in Autism Spectrum Disorder (ASD) and typically-growing children. A synchronization index is adapted for forming the edges of the connectivity graph capturing the stability of each of the synchrostates. Such network is formed for 11 ASD and 12 control group children. Comparative analyses of these networks using graph theoretic measures show that children with autism have a different modularity of such networks from typical children. This result could pave the way to a new modality for possible identification of ASD from non-invasively recorded EEG data

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More information

Published date: 8 November 2013
Venue - Dates: Neural Engineering (NER), 2013 6th International IEEE/EMBS Conference on, United States, 2013-11-06 - 2013-11-08
Organisations: Electronics & Computer Science

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Local EPrints ID: 360584
URI: https://eprints.soton.ac.uk/id/eprint/360584
PURE UUID: 63775952-feb5-4831-ab2f-9d5443a3b762

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Date deposited: 06 Jan 2014 13:23
Last modified: 06 Aug 2019 18:47

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