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Doctoral thesis
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Time-frequency Granger causality with application to nonstationary brain signals

ContributorsCekic, Sezen
Defense date2015-12-04
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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Citation (ISO format)
CEKIC, Sezen. Time-frequency Granger causality with application to nonstationary brain signals. 2015. doi: 10.13097/archive-ouverte/unige:78968
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Creation12/16/2015 4:31:00 PM
First validation12/16/2015 4:31:00 PM
Update time03/15/2023 12:00:56 AM
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