For information on PhD and postdoc positions in our group please contact Athina Tzovara: athina.tzovara@unibe.ch, including your CV.
How does the brain make sense of music? This project investigates the neural mechanisms underlying auditory perception by combining acoustic signal processing with analysis of brain activity recordings. To understand how the brain processes music, we will apply a range of signal processing and feature extraction methods to describe and annotate music. These representations will then be used within encoding and/or decoding models to analyse iEEG data, revealing how different sound properties are processed across the brain.
The student joining this project should be comfortable programming independently in Python and have some familiarity with basic signal processing techniques and/or time-series analysis. The project will develop practical skills in neural data analysis, acoustic signal processing, and encoding/decoding models.
For more information please contact: Athina Tzovara: athina.tzovara@unibe.ch Magdalena Kachlicka: magdalena.kachlicka@unibe.ch
Insomnia disorder is very prevalent worldwide and especially the elderly population suffers increasingly. The pitfalls of current insomnia medications is manifold, such as addiction and altered wakefulness. Increasingly these drugs have also been associated with unnatural sleep.
This master thesis will be integrated into a bigger project trying to better understand how and why these drugs alter sleep and wakefulness and to what extent. Specifically this project will use signal processing and established analysis techniques to characterise a dataset of overnight human electroencephalography (EEG) data of participants following administration of sleep drugs. Students will gain experience in data analysis, signal processing and sleep research.
For more information please contact: Athina Tzovara: athina.tzovara@unibe.ch
Studying neural functions in patients in a coma can be informative of their chances to regain consciousness. Electroencephalography (EEG) is a technique that allows to assess neural signals in patients in a coma in a non-invasive way. To analyse the rich data generated by EEG measurements novel data analysis and signal processing techniques are needed. In our work, we are studying oscillatory and non oscillatory patterns of EEG activity in coma patients. We will employ signal processing techniques to analyse rich EEG datasets and study their temporal patterns, with the goal of identifying predictors of the chances to awaken from a coma.
The project is suitable for a Master thesis project. The student working on it should be motivated to program indepentently in Python and to analyze EEG data. This project will give experience with data analysis, signal processing, neuroscience and working with clinical data.
For more information please contact: Athina Tzovara: athina.tzovara@unibe.ch
Intrinsic neural timescales can be understood as the characteristic durations over which neural activity remains correlated with itself. Timescales have emerged as a key organising principle of cortical computation, yet the methods used to estimate them vary in their assumptions and sensitivity. In this project, we will implement and systematically compare several timescale estimation methods (e.g. autocorrelation function, spectral methods), assessing their reliability, validity, and sensitivity to methodological choices.
The student working on this project should be motivated to program independently in Python and to analyse EEG and/or iEEG data. The project will develop practical skills in neural data analysis, reliability metrics, and model comparison.
For more information please contact: Athina Tzovara: athina.tzovara@unibe.ch Magdalena Kachlicka: magdalena.kachlicka@unibe.ch
Sleep disorders affect a large part of the human population, posing a major public health concern. However, diagnosing and treating sleep disorders remains today challenging. Polysomnography (PSG) allows us to study sleep, by recording signals of the brain via electroencephalography (EEG) and also of the heart (ECG) or respiration. The goal of this bachelor thesis is to assist in the analysis of a large dataset of PSG recordings in patients with central disorders of hypersomnolence and healthy controls.
The student working in this project will acquire experience with signal processing, analysing time series, sleep research, and working with a large and rich dataset. For this bachelor thesis you are expected to be able to program independently in Python.
For more information please contact: Athina Tzovara: athina.tzovara@unibe.ch