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Doctoral thesis
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English

Time-frequency Granger causality with application to nonstationary brain signals

Contributors

Cekic, Sezen

Directors

Renaud, Olivier; Grandjean, Didier Maurice

Defense date

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.

UNIGE affiliation entities

UNIGE research groups

Citation (ISO format)

CEKIC, Sezen. Time-frequency Granger causality with application to nonstationary brain signals. Doctoral Thesis, 2015. doi: 10.13097/archive-ouverte/unige:78968

Main files (1)

Thesis

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Technical informations

Creation

16/12/2015 16:31:00

First validation

16/12/2015 16:31:00

Update

13/10/2025 21:07:39

Status update

15/03/2023 00:00:55

Last indexation

30/05/2026 07:46:49
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