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

Tutkimustuotos: vertaisarvioituKonferenssiartikkeli

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoEMBEC 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
KustantajaSpringer-Verlag
Sivut763-766
Sivumäärä4
ISBN (painettu)9789811051210
TilaJulkaistu - 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
Tapahtuma - Tampere, Suomi

Julkaisusarja

NimiIFMBE Proceedings
KustantajaSpringer-Verlag
Vuosikerta65
ISSN (painettu)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
MaaSuomi
KaupunkiTampere
Ajanjakso11/06/201715/06/2017

Tutkijat

Organisaatiot

Kuvaus

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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