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Thesis and Lab Rotation Projects
Please check this page regularly, new topics will be added on a rolling basis. As long as there are no topics on this page yet, feel free to contact our group directly.
Comparison of FEM and BEM models for EEG forward and inverse modeling (BSc)
Target group: BSc Students in Computer Science or related fields
Short description: This project will integrate an existing finite element (FEM) modeling pipeline (ROAST) into the open source package Brainstorm for electroencephalographic (EEG) data analysis. This will make it possilbe to create accurate volume conductor models for brain source localization. The project will also quantitatively compare the obtained accuracy with that of standard boundary element method (BEM) modeling implemented in Brainstorm.
Background: Electrical volume conductor modeling of the head is an important step when it comes to modeling the effect of transcranial electric brain stimulation (TES) as well as localizing brain sources electroencephalographic (EEG) measurements. While TES modeling typically relies on detailed finite element (FEM) solvers, software packages for EEG inverse modeling typically offer only less accurate boundary-element (BEM) solvers. This project will make an existing FEM code (ROAST) accessible for EEG inverse modeling by integrating it into the open source package Brainstorm. This will allow for a direct quantitative comparison of FEM and BEM models in terms of EEG source localization accuracy.
Required skills: Programming experience
Optional skills: MATLAB, basic linear algebra
Anticipated duration: 3 months
Contact: Stefan Haufe
Development and validation of an individual head modeling pipeline for MEG source localization (BSc)
Target group: BSc Students in Computer Science or related fields
Short description: This project will develop an individual head modeling pipeline for magnetoencephalography (MEG), and will apply it for the purpose of localizing the sources of real MEG data.
Background: Electrical volume conductor modeling of the head is an important step when it comes to localizing brain sources magnetoencephalographic (MEG) measurements. Here it is important to take the individual anatomy of the subject's head and its relative position in the MEG scanner into account. This project will develop an individual head modeling pipeline for the Yokogawa MEG system at the PTB, and will test it using real MEG data.
Required skills: Programming experience
Optional skills: MATLAB, Python, basic linear algebra
Anticipated duration: 3 months
Contact: Stefan Haufe
Investigating the relationship between power and functional connectivity of brain rhythms (MSc)
Target group: MSc Students in Computational Neuroscience or related fields
Short description: This project will study the relationship between the power of rhythmic brain signals and the functional connectivity (coherence, Granger causality) between such signals through theoretical analyses and simulations.
Background: While it is often observed that the synchronization of brain rhythms correlates with their strengths, the relationship between the two can be much more complex. Similarly, directed and undirected functional connectivity metrics can lead to seemingly inconsistent results. The purpose of this project is to derive simple examples that illustrate the complex ways in which power and connectivity can interact.
Required skills: Programming, signal processing
Optional skills: MATLAB, Python
Anticipated duration: 6 months
Contact: Stefan Haufe
Investigating the effect of whitening on "AI explanation performance" (MSc)
Target group: MSc Students in Computer Science or related fields
Short description: This project will study the effects of various whitening and orthogonalization transforms of the input data on the "explanation performance" of so called "explainable AI" methods.
Background: As machine learning and artificial intelligence methods are increasingly used in sensitive applications, a need for such methods to be interpretable to humans has arisen, leading to the formation of the field of "explainable AI" (XAI). However, most XAI methods do not address a well-defined problem and are hence difficult to benchmark. The UNIML group has started to provide problem definitions, benchmarks and performance metrics for assessing "explanation performance". This project will explore the ability of whitening transforms to improve the performance of popular XAI methods.
Required skills: Python, machine learning
Optional skills: experience with deep learning and XAI frameworks
Anticipated duration: 6 months
Contact: Stefan Haufe
Development of novel techniques to "explain" nonlinear prediction models (MSc)
Target group: MSc Students in Computer Science or related fields
Short description: This project will develop and implement novel techniques to "explain" specific classes of non-linear prediction models.
Background: As machine learning and artificial intelligence methods are increasingly used in sensitive applications, a need for such methods to be interpretable to humans has arisen, leading to the formation of the field of "explainable AI" (XAI). However, most XAI methods do not address a well-defined problem and are hence difficult to benchmark. The UNIML group has started to provide problem definitions, benchmarks and performance metrics for assessing "explanation performance". This project will propose novel techniques to derived explanations and interpretations from nonlinear models. In particular, we will be concerned with kernel methods and/or deep neural networks. Existing non-linear benchmark problems from the group will be used to benchmark the proposed approach and guide their further refinement.
Required skills: Python, machine learning, statistics
Optional skills: experience with deep learning frameworks
Anticipated duration: 6 months
Contact: Stefan Haufe