Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Tracking of dynamic functional connectivity from MEG data with Kalman filtering

Tronarp, Filip LU ; Parkkonen, Lauri ; Särkkä, Simo and Subramaniyam, Narayan P (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:
author
; ; and
publishing date
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
978-1-5386-3646-6
978-1-5386-3645-9
978-1-5386-3647-3
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
2024-05-04 02:12:14
@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         = {{978-1-5386-3646-6}},
  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}},
}