Tracking of dynamic functional connectivity from MEG data with Kalman filtering
(2018) 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)- Abstract
- Owing to their millisecond-scale temporal resolution, magnetoencephalography (MEG) and electroencephalography (EEG) are well-suited tools to study dynamic functional connectivity between regions in the human brain. However, current techniques to estimate functional connectivity from MEG/EEG are based on a two-step approach; first, the MEG/EEG inverse problem is solved to estimate the source activity, and second, connectivity is estimated between the sources. In this work, we propose a method for simultaneous estimation of source activities and their dynamic functional connectivity using a Kalman filter. Based on simulations, our approach can reliably estimate source activities and resolve their time-varying interactions even at low SNR (
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/0dd9b37a-37b6-483d-94f9-418c8da38ccf
- author
- Tronarp, Filip LU ; Parkkonen, Lauri ; Särkkä, Simo and Subramaniyam, Narayan P
- publishing date
- 2018
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- conference location
- Honolulu, United States
- conference dates
- 2018-07-18 - 2018-07-21
- external identifiers
-
- scopus:85056651048
- ISBN
- 978-1-5386-3647-3
- 978-1-5386-3646-6
- 978-1-5386-3645-9
- DOI
- 10.1109/EMBC.2018.8512456
- language
- English
- LU publication?
- no
- id
- 0dd9b37a-37b6-483d-94f9-418c8da38ccf
- date added to LUP
- 2023-08-20 22:49:19
- date last changed
- 2024-08-24 13:07:11
@inproceedings{0dd9b37a-37b6-483d-94f9-418c8da38ccf, abstract = {{Owing to their millisecond-scale temporal resolution, magnetoencephalography (MEG) and electroencephalography (EEG) are well-suited tools to study dynamic functional connectivity between regions in the human brain. However, current techniques to estimate functional connectivity from MEG/EEG are based on a two-step approach; first, the MEG/EEG inverse problem is solved to estimate the source activity, and second, connectivity is estimated between the sources. In this work, we propose a method for simultaneous estimation of source activities and their dynamic functional connectivity using a Kalman filter. Based on simulations, our approach can reliably estimate source activities and resolve their time-varying interactions even at low SNR (}}, author = {{Tronarp, Filip and Parkkonen, Lauri and Särkkä, Simo and Subramaniyam, Narayan P}}, booktitle = {{40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}}, isbn = {{978-1-5386-3647-3}}, language = {{eng}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Tracking of dynamic functional connectivity from MEG data with Kalman filtering}}, url = {{http://dx.doi.org/10.1109/EMBC.2018.8512456}}, doi = {{10.1109/EMBC.2018.8512456}}, year = {{2018}}, }