The University of Southampton
University of Southampton Institutional Repository

Existence of millisecond-order stable states in time-varying phase synchronization measure in EEG signals

Existence of millisecond-order stable states in time-varying phase synchronization measure in EEG signals
Existence of millisecond-order stable states in time-varying phase synchronization measure in EEG signals
In this paper, we have developed a new measure of understanding the temporal evolution of phase synchronization for EEG signals using cross-electrode information. From this measure it is found that there exists a small number of well-defined phase-synchronized states, each of which is stable for few milliseconds during the execution of a face perception task. We termed these quasi-stable states as synchrostates. We used k-means clustering algorithms to estimate the optimal number of synchrostates from 100 trials of EEG signals over 128 channels. Our results show that these synchrostates exist consistently in all the different trials. It is also found that from the onset of the stimulus, switching between these synchrostates results in well-behaved temporal sequence with repeatability which may be indicative of the dynamics of the cognitive process underlying that task. Therefore these synchrostates and their temporal switching sequences may be used as a new measure of the stability of phase synchrony and information exchange between different regions of a human brain.
brain dynamics, CWT, EEG, k-means clustering, phase synchronization, synchrostate
2539-2542
Jamal, Wasifa
3f70176e-843e-46b7-8447-4eefaef104f1
Das, Saptarshi
e06f2eb0-1e3e-453c-ba78-82eed18ceac9
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Jamal, Wasifa
3f70176e-843e-46b7-8447-4eefaef104f1
Das, Saptarshi
e06f2eb0-1e3e-453c-ba78-82eed18ceac9
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd

Jamal, Wasifa, Das, Saptarshi and Maharatna, Koushik (2013) Existence of millisecond-order stable states in time-varying phase synchronization measure in EEG signals. 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Japan. 03 - 07 Jul 2013. pp. 2539-2542 . (doi:10.1109/EMBC.2013.6610057).

Record type: Conference or Workshop Item (Paper)

Abstract

In this paper, we have developed a new measure of understanding the temporal evolution of phase synchronization for EEG signals using cross-electrode information. From this measure it is found that there exists a small number of well-defined phase-synchronized states, each of which is stable for few milliseconds during the execution of a face perception task. We termed these quasi-stable states as synchrostates. We used k-means clustering algorithms to estimate the optimal number of synchrostates from 100 trials of EEG signals over 128 channels. Our results show that these synchrostates exist consistently in all the different trials. It is also found that from the onset of the stimulus, switching between these synchrostates results in well-behaved temporal sequence with repeatability which may be indicative of the dynamics of the cognitive process underlying that task. Therefore these synchrostates and their temporal switching sequences may be used as a new measure of the stability of phase synchrony and information exchange between different regions of a human brain.

Text
EMBC_Wasifa_camera_ready.pdf - Author's Original
Download (964kB)

More information

Published date: 3 July 2013
Venue - Dates: 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Japan, 2013-07-03 - 2013-07-07
Keywords: brain dynamics, CWT, EEG, k-means clustering, phase synchronization, synchrostate
Organisations: Electronic & Software Systems

Identifiers

Local EPrints ID: 354936
URI: https://eprints.soton.ac.uk/id/eprint/354936
PURE UUID: 0bba3f38-d122-46cd-b80f-96a337942ce2

Catalogue record

Date deposited: 07 Aug 2013 14:28
Last modified: 28 Aug 2019 18:52

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×