EEG Semester Project
The goal of this project is to re-analyse and document a real EEG dataset using MNE-Python.
How I will grade the projects can be seen in grading. I value line of thinking and documented motivation much higher than actual results. I want to see that you understand what you are doing.
Which Datasets?
In previous iterations, we took a well understood and standardized EEG dataset, which made the task a bit unrealistic and, frankly, a bit boring. Therefore, you will choose an EEG paper (with data) to reproduce yourself.
This has the consequence that there will be many (individual) unforeseen problems, inconsistencies and problems. This is to be expected and very typical for EEG analyses. See it as part of the challenge!
What follows is a list of datasets with accompanying papers that I have screened. You can select one of these if you wish, but also feel free to look for your own datasets either on nemar.org or on openneuro.org. Try to look for datasets below 20Gb of space, with a paper accompanied. Please send a potential candidate to me asap, so I can screen it.
| Subjects | task | analysis | link |
|---|---|---|---|
| 24 | pattern symmetry | ERP | dataset, paper |
| 18 | walking oddball | timefreq | dataset, paper, dataset-paper |
| 47 | social task | timefreq | dataset, paper |
| 17 | game-playing | ERP | dataset, paper |
| 48 | vr visual tasks | ERP | dataset, paper |
| 62 | competitive decision-making | decoding | dataset, paper |
| 50 | Parkinsonโs classification | ERP; decoding | dataset, paper |
| 25 | cocktail party | ERP; encoding | dataset, paper |
What should the project contain?
The idea is to reproduce, but not with a direct reproduction. That is, we will not try to re-do their exact pipeline step-by-step, but rather we will try to obtain their result, with a different pipeline, checking for the robustness of the original analysis pipeline.
A list of the typical steps you will perform:
- Preprocessing: Filtering, re-referencing, ICA, Event-handling
- (automatic) data cleaning: Time, channel and subjects
Group work
Given the new nature of the datasets and the unforeseeable complications, I propose student groups of 2-3 students.
Where do I get the data?
You can find the link above.
Tips
- Please work modularly! Donโt put all your functions in a single jupyter-notebook. Please use notebooks for your analysis & organise your functions into ./src/xyz.py files.
- Make use of the BIDS structure and mne-bids-pipeline.
- Thoroughly think about what you are doing and why. And note that down - most people can blindly apply a pipeline, I want to see your reasoning why you included / removed certain steps.
How can I get help / advice?
- Write in the ILIAS Forum. Either others will help, or I myself will answer questions.
- I highly recommend watching the first talk in this session: https://www.crowdcast.io/e/live-meeg-2020/7 by Marijn van Vliet for a fitting introduction of analysing multiple subjects with MNE in a reproducible and documented way. After that, use mne-bids-pipeline as suggested before.
- The MNE-documentation is quite extensive. It is worth looking into. You can also try stackexchange or neurostar for help.
FAQ (will be continuously updated)
Format of submission
You can share a git of your project, or the code via ILIAS. The git can be on github or on the university github.
You make my life easier if you name the git / zip file with your last name, e.g. Muller_SS2026_EEGSemesterProject.zip, and the folder containing in the zip as well.
Deadline
Please hand in the code + poster until 10.03.2027 - 7 days before the poster presentation, so we can check the code and print the posters in time.