Evolutionary State-Space Model and Its Application to Time-Frequency Analysis of Local Field Potentials

DOI10.5705/ss.202017.0420zbMath1453.62724arXiv1610.07271OpenAlexW3100854855WikidataQ129210884 ScholiaQ129210884MaRDI QIDQ5134489

Weining Shen, Hernando Ombao, Babak Shahbaba, Norbert J. Fortin, Xu Gao

Publication date: 16 November 2020

Published in: Statistica Sinica (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1610.07271

zbMATH Keywords

spectral analysisautoregressive modelstate-space modelstime-frequency analysisbrain signals

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Inference from stochastic processes and spectral analysis (62M15)

Related Items (8)

Brain waves analysis via a non-parametric Bayesian mixture of autoregressive kernels ⋮ Modeling dependence via copula of functionals of Fourier coefficients ⋮ Regularized matrix data clustering and its application to image analysis ⋮ Nonparametric matrix response regression with application to brain imaging data analysis ⋮ Conex-connect: learning patterns in extremal brain connectivity from multichannel EEG data ⋮ Geodesic Lagrangian Monte Carlo over the space of positive definite matrices: with application to Bayesian spectral density estimation ⋮ Coherence-based time series clustering for statistical inference and visualization of brain connectivity ⋮ Clustering brain signals: a robust approach using functional data ranking

Uses Software

• EEGLAB
• astsa
• HIBITS
• HINT

Cites Work

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• Exact prior-free probabilistic inference on the heritability coefficient in a linear mixed model
• Sequential parameter learning and filtering in structured autoregressive state-space models
• Modeling binary time series using Gaussian processes with application to predicting sleep states
• Consistency of the maximum likelihood estimator for general hidden Markov models
• A General Probabilistic Model for Group Independent Component Analysis and Its Estimation Methods
• How well does the finite Fourier transform approximate the Fourier transform?

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