Expectation–maximization algorithm with a nonlinear kalman smoother for MEG/EEG connectivity estimation
(2017) Joint Conference of the European Medical and Biological Engineering Conference, EMBEC 2017 and Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2107 In IFMBE Proceedings 65. p.763-766- Abstract
Current techniques to determine functional or effective connectivity from magnetoencephalography (MEG) and electroencephalography (EEG) signals typically involve two sequential steps: 1) estimation of the source current distribution from the sensor data, for example, by minimum-norm estimation or beamforming, and 2) fitting a multivariate autoregressive (MVAR) model to estimate the AR coefficients, which reflect the interaction between the sources. Here, we introduce a combination of the expectation–maximization (EM) algorithm and a nonlinear Kalman smoother to perform joint estimation of both source and connectivity (linear and nonlinear) parameters from MEG/EEG signals. Based on simulations, we show that the proposed approach... (More)
Current techniques to determine functional or effective connectivity from magnetoencephalography (MEG) and electroencephalography (EEG) signals typically involve two sequential steps: 1) estimation of the source current distribution from the sensor data, for example, by minimum-norm estimation or beamforming, and 2) fitting a multivariate autoregressive (MVAR) model to estimate the AR coefficients, which reflect the interaction between the sources. Here, we introduce a combination of the expectation–maximization (EM) algorithm and a nonlinear Kalman smoother to perform joint estimation of both source and connectivity (linear and nonlinear) parameters from MEG/EEG signals. Based on simulations, we show that the proposed approach estimates both the source signals and AR coefficients in linear models significantly better than the traditional two-step approach when the signal-to-noise ratio (SNR) is low (≤1) and gives comparable results at higher SNRs (>1). Additionally, we show that nonlinear interaction parameters can be reliably estimated from MEG/EEG signals at low SNRs using the EM algorithm with sigma-point Kalman smoother.
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- author
- Subramaniyam, Narayan Puthanmadam ; Tronarp, Filip LU ; Särkkä, Simo and Parkkonen, Lauri
- publishing date
- 2017
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- EEG, Expectation-maximization, Functional connectivity, MEG, Nonlinear Kalman smoother
- host publication
- EMBEC and NBC 2017 - Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2017
- series title
- IFMBE Proceedings
- editor
- Eskola, Hannu ; Vaisanen, Outi ; Viik, Jari and Hyttinen, Jari
- volume
- 65
- pages
- 4 pages
- publisher
- Springer
- conference name
- Joint Conference of the European Medical and Biological Engineering Conference, EMBEC 2017 and Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2107
- conference location
- Tampere, Finland
- conference dates
- 2017-06-11 - 2017-06-15
- external identifiers
-
- scopus:85021750408
- ISSN
- 1680-0737
- ISBN
- 9789811051210
- DOI
- 10.1007/978-981-10-5122-7_191
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © Springer Nature Singapore Pte Ltd. 2018.
- id
- 5e28273d-2752-43aa-9c22-b005e8bc9c33
- date added to LUP
- 2023-08-23 15:49:31
- date last changed
- 2023-10-20 10:35:31
@inproceedings{5e28273d-2752-43aa-9c22-b005e8bc9c33, abstract = {{<p>Current techniques to determine functional or effective connectivity from magnetoencephalography (MEG) and electroencephalography (EEG) signals typically involve two sequential steps: 1) estimation of the source current distribution from the sensor data, for example, by minimum-norm estimation or beamforming, and 2) fitting a multivariate autoregressive (MVAR) model to estimate the AR coefficients, which reflect the interaction between the sources. Here, we introduce a combination of the expectation–maximization (EM) algorithm and a nonlinear Kalman smoother to perform joint estimation of both source and connectivity (linear and nonlinear) parameters from MEG/EEG signals. Based on simulations, we show that the proposed approach estimates both the source signals and AR coefficients in linear models significantly better than the traditional two-step approach when the signal-to-noise ratio (SNR) is low (≤1) and gives comparable results at higher SNRs (>1). Additionally, we show that nonlinear interaction parameters can be reliably estimated from MEG/EEG signals at low SNRs using the EM algorithm with sigma-point Kalman smoother.</p>}}, author = {{Subramaniyam, Narayan Puthanmadam and Tronarp, Filip and Särkkä, Simo and Parkkonen, Lauri}}, booktitle = {{EMBEC and NBC 2017 - Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2017}}, editor = {{Eskola, Hannu and Vaisanen, Outi and Viik, Jari and Hyttinen, Jari}}, isbn = {{9789811051210}}, issn = {{1680-0737}}, keywords = {{EEG; Expectation-maximization; Functional connectivity; MEG; Nonlinear Kalman smoother}}, language = {{eng}}, pages = {{763--766}}, publisher = {{Springer}}, series = {{IFMBE Proceedings}}, title = {{Expectation–maximization algorithm with a nonlinear kalman smoother for MEG/EEG connectivity estimation}}, url = {{http://dx.doi.org/10.1007/978-981-10-5122-7_191}}, doi = {{10.1007/978-981-10-5122-7_191}}, volume = {{65}}, year = {{2017}}, }