Skip navigation

Identification of time-varying cortico-cortical and cortico-muscular coherence during motor tasks with multivariate autoregressive models

Identification of time-varying cortico-cortical and cortico-muscular coherence during motor tasks with multivariate autoregressive models

Xifra-Porxas, Alba ORCID: 0000-0002-9023-2432, Kostoglou, Kyriaki, Lariviere, Sara, Niso, Guiomar, Kassinopoulos, Michalis, Boudrias, Marie-Helene and Mitsis, Georgios D. (2018) Identification of time-varying cortico-cortical and cortico-muscular coherence during motor tasks with multivariate autoregressive models. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, pp. 1021-1024. ISBN 978-1538636466 ISSN 1557-170X (Print), 1558-4615 (Online) (doi:https://doi.org/10.1109/EMBC.2018.8512475)

[img]
Preview
PDF (Author's Accepted Manuscript)
29489 XIFRA-PORXAS_Identification_Of_Time-varying_Cortical_And_Muscular_Coherence_(AAM)_2018.pdf - Accepted Version

Download (478kB) | Preview

Abstract

Neural populations coordinate at fast subsecond time-scales during rest and task execution. As a result, functional brain connectivity assessed with different neuroimaging modalities (EEG, MEG, fMRI) may also change over different time scales. In addition to the more commonly used sliding window techniques, the General Linear Kalman Filter (GLFK) approach has been proposed to estimate time-varying brain connectivity. In the present work, we propose a modification of the GLFK approach to model time-varying connectivity. We also propose a systematic method to select the hyper-parameters of the model. We evaluate the performance of the method using MEG and EMG data collected from 12 young subjects performing two motor tasks (unimanual and bimanual hand grips), by quantifying time-varying cortico-cortical and cortico-muscular coherence (CCC and CMC). The CMC results revealed patterns in accordance with earlier findings, as well as an improvement in both time and frequency resolution compared to sliding window approaches. These results suggest that the proposed methodology is able to unveil accurate time-varying connectivity patterns with an excellent time resolution.

Item Type: Conference Proceedings
Title of Proceedings: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Uncontrolled Keywords: cortico-muscular coherence, magnetoencephalography electromyography, time-frequency analysis, Kalman filters
Subjects: B Philosophy. Psychology. Religion > BF Psychology
Faculty / Department / Research Group: Faculty of Education, Health & Human Sciences
Last Modified: 25 Sep 2020 11:23
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/29489

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics