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Time-frequency Granger causality with application to nonstationary brain signals

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Defense Thèse de doctorat : Univ. Genève, 2015 - GSEM 18 - 2015/12/04
Abstract This PhD thesis concerns the modelling of time-varying causal relationships between two signals, with a focus on signals measuring neural activities. The ability to compute a dynamic and frequency-specific causality statistic in this context is essential and Granger causality provides a natural statistical tool. In Chapter 1 we propose a review of the existing methods allowing one to measure time-varying frequency-specific Granger causality and discuss their advantages and drawbacks. Based on this review, we propose in Chapter 2 an estimator of a linear Gaussian vector autoregressive model with coefficients evolving over time. Estimation procedure is achieved through variational Bayesian approximation and the model provides a dynamical Granger-causality statistic that is quite natural. We propose an extension to the `a trous Haar decomposition that allows us to derive the desired dynamical and frequency-specific Granger-causality statistic. In Chapter 3 we propose an application of the model to real experimental data.
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URN: urn:nbn:ch:unige-789688
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Thesis (7.2 MB) - public document Free access
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Research group Méthodologie et analyse des données (MAD)
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CEKIC, Sezen. Time-frequency Granger causality with application to nonstationary brain signals. Université de Genève. Thèse, 2015. https://archive-ouverte.unige.ch/unige:78968

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Deposited on : 2016-01-04

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