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Expectation–maximization algorithm with a nonlinear kalman smoother for MEG/EEG connectivity estimation

Subramaniyam, Narayan Puthanmadam ; Tronarp, Filip LU ; Särkkä, Simo and Parkkonen, Lauri (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
; ; and
publishing date
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 (&gt;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}},
}