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

Research output: Scientific - peer-reviewConference contribution

Details

Original languageEnglish
Title of host publicationEMBEC 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
PublisherSpringer-Verlag
Pages763-766
Number of pages4
ISBN (Print)9789811051210
StatePublished - 2017
MoE publication typeA4 Article in a conference publication
EventJoint Conference of the European Medical and Biological Engineering Conference, EMBEC 2017 and Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2107 - Tampere, Finland
Duration: 11 Jun 201715 Jun 2017

Publication series

NameIFMBE Proceedings
PublisherSpringer-Verlag
Volume65
ISSN (Print)1680-0737

Conference

ConferenceJoint Conference of the European Medical and Biological Engineering Conference, EMBEC 2017 and Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2107
CountryFinland
CityTampere
Period11/06/201715/06/2017

Researchers

Research units

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 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.

    Research areas

  • EEG, Expectation-maximization, Functional connectivity, MEG, Nonlinear Kalman smoother

ID: 14299881