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

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.