Thesis (7.2 MB) - 
Free access
Highlights
Doctoral Thesis
unige:78968 
 |
Time-frequency Granger causality with application to nonstationary brain signals |
|
| Author | ||
| Directors | ||
| 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. | |
| Identifiers | URN: urn:nbn:ch:unige-789688 | |
| Full text | ||
| Structures | ||
| Research groups | Affective sciences Méthodologie et analyse des données (MAD) | |
| Citation (ISO format) | CEKIC, Sezen. Time-frequency Granger causality with application to nonstationary brain signals. Université de Genève. Thèse, 2015. doi: 10.13097/archive-ouverte/unige:78968 https://archive-ouverte.unige.ch/unige:78968 |
704 hits 
237 downloads 
Contact an author
Update