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Master theses

Current and past ideas and concepts for Master Theses.

Extracting relevant features from neuroimaging data using compressive sensing


The problem of reconstructing nonlinear and complex dynamical systems from measured data or time series is central to many scientific disciplines including physical, biological, computer, and social sciences, as well as engineering and economics. Compressive sensing is a powerful technique able to perform data selection and reduction, extracting relevant feature from noisy and short time series.

Kind of work

Recent studies [1] suggest that cross-frequency coupling may serve a functional role in neuronal computation, communication, and learning. The strength of phase-amplitude CFC differs across brain areas in a task-relevant manner, changes quickly in response to sensory, motor, and cognitive events, and correlates with performance in learning tasks. In this thesis work we will explore the application of compressive sensing to detect cross-frequency coupling, a relevant yet elusive concept with main application to the analysis of neurophysiological time series. In this study, we will first build a simple model of interactions at different scales using toy models for the ground truth, then we will test the algorithm on electroencephalography data from publicly available datasets.

Framework of the Thesis

1) Ryan T. Canolty and Robert T. Knight, “The functional role of cross-frequency coupling”, Trends Cogn Sci. 14(11), pp: 506–515, 2010.
2) Wenxu Wang, Ying-Cheng Lai, Celso Grebogi, “Data Based Identification and Prediction of Nonlinear and Complex Dynamical Systems”:
3) Celso Grebogi, “Time-Series Based Prediction of Dynamical Systems and Complex Networks”:

In collaboration with Prof. Daniele Marinazzo (UGhent-Department of data Analysis)

Expected Student Profile

• Following an MSc in a field related to one or more of the following: electrical engineering, computer science, applied mathematics, Biomedical Engineering
• Experience with signal processing, machine learning and statistics.
• Strong programming skills.
• Ability to write scientific reports and communicate research results at conferences in English.


Prof. Hichem Sahli

+32 (0)2 629 2916

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