Thesis/Project Openings

Open M.D. Theses

Epileptic seizures are detected clinically using an Electroencephalogram (EEG). Since home-usage of such devices for early seizure onset warning is currently not possible, the aim of this work is the analysis of alternative, EEG-less methods for seizure detection or prediction.
The work of this thesis is split in four parts:
1. Analysis of existing methods for EEG-less seizure detection
2. Assembly of required sensors and preparation of a clinical trial
3. Clinical trial
4. Analysis.
We recommend to perform part 1 & 2 prior to the “Freisemester”.


KONTAKT
Stephan Jonas
sjonas@mi.rwth-aachen.de

Automatic (computer-based) prediction of epileptic seizures can bring great benefits to the patients. There is currently a lot of research going on the topic, where primary EEG, but also e.g. EMG data has been used. We propose a retrospective study of video EEG data collected in Epileptology UKA. The task of the MD student is to extract the relevant data from the records and in collaboration with a data analyst try to identify individual patterns which might be used for seizure detection.


Open Bachelor’s Theses

The IMCAR at the Uniklinik RWTH Aachen is developing a novel microscopy modality for imaging muscle traction. In this thesis, the muscle traction captured on the microscopy images should be analyzed and quantified in a meaningful way (i.e., flow charts, etc).


KONTAKT
Stephan Jonas
sjonas@mi.rwth-aachen.de

Open Master’s Theses

Open Student Projects

To analyze impact of articles, the number of references is a good measure. The more cited an article is, the more influential it is. However, influence can also be traversed through subsequent citation. The aim of this work is therefore a tool to visualize citation networks of articles and cross-references.


KONTAKT
Stephan Jonas
sjonas@mi.rwth-aachen.de

The aim of this work is to use available mobile 3D reconstruction applications and evaluation the reconstruction quality based on several 3D-printed dummy shapes.


KONTAKT
Stephan Jonas
sjonas@mi.rwth-aachen.de

In the clinical study two EEG (electroencephalographic) measurements were performed simultaneously. One was done with the help of standard clinical device while for the other one a low-cost mobile device (Emotiv Epoc) was used. In order to compare two multivariate time series we propose to build a separate Auto-regressive model for each EEG record, compare parallel records and analyse the statistics on the model coefficients across the patients.