Uncertainty, Inverse Modeling and Machine Learning

Thesis and Labrotation 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.

Available Topics

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 relationsship 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 illustrative 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

Development of novel techniques to interpret 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

Enumeration of modes in generated data

Enumeration of modes in generated data

Title: Enumeration of modes in generated data

Target group: BSc/MSc Students in Computer Science or related fields

Short description: Find data patterns inside high dimensional time series data to detect bad generative model

Background:

The rise of generative models, such as MidJourney, has brought about significant advancements in the field of machine learning. These models have shown impressive capabilities in creating synthetic data with a wide range of possible applications, including data augmentation, data privacy, and data sharing. However, as generative models become more prevalent, they also raise important ethical and social issues that need to be carefully considered.

 

In this bachelor's thesis, we aim to investigate one important aspect of generative modeling: the enumeration of possible synthetic data modes or prototypes. This process involves identifying and describing the different variations of synthetic data that can be generated by a given generative model. By enumerating these modes, we can gain a better understanding of the types of synthetic data that can be produced, which can be useful as a quality criteria for synthetic data.

 

The thesis will consist of a few components: writing an expose, which explores the existing literature for possible solutions, either applying or adapting it to the time series data, and developing own method

Required skills: strong background in statistics/data analysis/machine learning

Anticipated duration: 3 months (or more, depending on deliverables)

Assigned Topics

Gesture recognition and classification using wearable sensors (BSc/MSc)

Target group: BSc/MSc Students in Computer Science or related fields

Short description: Using sensors on your wearable device (e.g. Android phone, Arduino with gyro- and accelerometer), fuse the data from gyroscope and accelerometer and create a speller.

Background: Currently there is an increasing interest in healthy lifestyle. An accessible way to improve the lifestyle is to use mobile apps and sophisticated sensors of the smartphones to gather structured information about wellbeing. In this project students will develop an application that uses wearable sensors for recognition and tracking of human activities. The scope of the human activities will depend on the project duration and preparedness of students. The simplest example would be a gesture speller, holding smartphone. A more complicated instance would be tracking of behavioural patterns and activities (eating, sleeping, working, etc). The project consists of 3 main parts: recording of the dataset, data processing and analysis, and application delivery.

Required skills: basic programmingin OOP language and basic knowledge of operating systems

Optional skills: Android programming, signal analysis 

Anticipated duration: 3 months (or more, depending on deliverables) 

Contact: Rustam Zhumagambetov

Towards robust metrics of amplitude-amplitude coupling between brain areas (MSc)

Target group: MSc Students in Computational Neuroscience or related fields

Short description: This project will conduct simulations to study the influence of source mixing on estimates of amplitude-amplitude coupling (AAC) between neural time series.

Background: The analysis of electrophysiological recordings of brain activity using electroencephalography (EEG) or similar techniques promises to shed light on the working principles of the brain. In particular, measures of interaction between neural time series may provide insight on how communication between different regions is implemented in the brain. One mechanism that has been proposed is through correlation of the envelopes of distinct brain rhythms (AAC). However, ubiquitous source mixing can induce spurious AAC. While remedies have been proposed, these can be demostrated to fail in counterexamples. This project aims to characterize the ability of different metrics of AAC to distinguish true from spurious across-site interaction. It will also aim to develop novel metrics based on antisymmetrizes higher order spectra.

Required skills: Programming, signal processing

Optional skills: MATLAB, Python

Anticipated duration: 6 months

Contact: Stefan Haufe

Characterizing dementia types using normative models of functional brain connectivity (MSc)

Target group: MSc Students in Neuroscience or related fields 

Short description: This project will analyze several large magnetoencephalography datasets comprising data of patients diagnosed with different stages and types of dementia. Robust functional connectivity estimation pipelines will be used to compare patients to previously established normative data from healthy subjects in order to identify clinically relevant clusters of patients.  

Background: Several devastating aging related neurological disorders such as Alzheimer's disease and other dementias are currently incurable and their pathophysiology is not well understood. Brain communication patterns in these disorders are likely disturbed making functional brain connectivity (FC) analysis a promising tool to derive disease and disease stage specific biomarkers. Ideally, such direct markers of brain functioning could even be of prognostic value and inspire novel interventions. In this project, we will apply validated robust pipelines for directed and undirected FC estimation to large patient MEG datasets. Comparisons to previously established normative data will be used to identify spatially and spectrally resolved FC markers that are specific to diseases and disease stages.  

Required skills: Matlab, signal processing, basic statistics, interest in the pathophysiology of neurological disorders 
Optional skills: Experience with M/EEG data analysis including source reconstruction and functional connectivity estimation 

Anticipated duration: 6+ months

Contact: Stefan Haufe

Design of benchmark data to validate explainable artificial intelligence (MSc)

Target group: MSc Students in Computer Science or related fields

Short description: This project will develop synthetic ground-truth data to benchmark and validate explainable artificial intelligence methods using generative deep learning 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 and performance metrics for assessing "explanation performance". This project will design and validate realistic yet well-defined ground-truth data to benchmark XAI approaches according to the developed definitions and criteria. To this end, we will use state-of-the-art generative models such as generative adversarial and diffusion models. The focus will be on natural and medical images.  

Required skills: Python, machine learning, statistics 
Optional skills: experience with deep learning frameworks

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

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