Mining cross-frequency coupling microstates (CFCμstates) from EEG recordings during resting state and mental arithmetic tasks

Dimitriadis, Stavros ORCID: https://orcid.org/0000-0002-0000-5392, Sun, Yu, Thakor, Nitish and Bezerianos, Anastasios 2016. Mining cross-frequency coupling microstates (CFCμstates) from EEG recordings during resting state and mental arithmetic tasks. Presented at: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, 16-20 August 2016. Published in: Patton, J., Barbieri, R., Ji, J., Jabbari, E., Dokos, S., Mukkamala, R., Jovanov, E., Dhaher, Y., Panescu, D., van Gils, M., Wheeler, B. and Dhawan, A. P. eds. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016): Proceedings of a meeting held 16-20 August 2016, Orlando, Florida, USA. Piscataway, NJ: IEEE, pp. 5517-5520. 10.1109/EMBC.2016.7591976

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Abstract

The functional brain connectivity has been studied by analyzing synchronization between dynamic oscillations of identical frequency or between different frequencies of distinct brain areas. It has been hypothesized that cross-frequency coupling (CFC) between different frequency bands is the carrier mechanism for the coordination of global and local neural processes and hence supports the distributed information processing in the brain. In the present study, we attempt to study the dynamic evolution of CFC at resting-state and during a mental task. The concept of CFC microstates (CFCμstates) is introduced as emerged short-lived patterns of CFC. We analyzed dynamic CFC (dCFC) at resting-state and during a comparison task by adopting a phase-amplitude coupling (PAC) estimator for [δ phase-γ-amplitude] coupling at every sensor. Modifying a well-established framework for mining brain dynamics, we show that a small sized repertoire of CFCμstates can be derived so as to encapsulate connectivity variations and further provide novel insights into network's functional reorganization. By analyzing the transition dynamics among CFCμstates, in both tasks, we provided a clear evidence about intrinsic networks that may play a crucial role in information integration.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Psychology
Publisher:	IEEE
ISBN:	9781457702204
ISSN:	1558-4615
Date of First Compliant Deposit:	25 July 2017
Date of Acceptance:	22 April 2016
Last Modified:	02 Nov 2022 11:43
URI:	https://orca.cardiff.ac.uk/id/eprint/102906

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