Marius Gerdes, M.Sc.

Marius Gerdes, M.Sc.

  • Engesserstr. 5
    76131 Karlsruhe

M. Sc. Marius Gerdes


  • Born 1993 in Heidelberg, Germany
  • Stay abroad in Norway from 2004-2012
  • Support Engineer at Apium Additive Technologies 2014-2020
  • M.Sc. Mechanical Engineering (2020)
    • Specialization Robotics and Mechatronics
    • MA Thesis: “A Rapid Prototyping Approach to EEG Electrodes for Use in Machine Learning Supported Brain Computer Interfaces”
  • Part of ITIV with the Helmholtz Project “MetisNeurotec” since 2021


Research Areas

  • Brain-Computer-Interfaces (BCI) and EEG
    BCIs connect the brain to the computer. There are different approaches to achieve this, from electrode array implants, to non-invasive EEG approaches and newer approaches such as functional near infrared spectroscopy.
  • Data analytics and machine learning
    Data collected from sources such as EEG, EKG etc. requires analysis to determine patterns and derive approaches for using the former in an applied context. Here we aim to use a combination of data analytics from statistics (e.g. standard FFT, CCA, TRCA) and machine learning to gain new insights into the data we have collected.
  • Dementia
    Dementia is a group of neurodegenerative conditions often associated with memory impairment. While most forms of dementia cannot be healed, studies such as the FINGER-Study have revealed that targeted changes in lifestyle are able to influence the progression. With a technical background we are aiming to transfer technologies into monitoring and assisting in the treatment of dementias.
  • Rapid prototyping
    Many ideas reach a stage when software is not all it takes. Rapid prototyping of hardware enables us to swiftly implement hardware part of project. We focus to leverage rapid prototyping for production of digital/analog circuitry and additive manufacturing.



Conference Papers
Towards EEG-based objective ADHD diagnosis support using convolutional neural networks
Stock, S.; Hausberg, J.; Armengol-Urpi, A.; Kaufmann, T.; Schinle, M.; Gerdes, M.; Stork, W.
2023. 2023 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Eindhoven, Netherlands, 29-31 August 2023, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/CIBCB56990.2023.10264876
Model-Driven Dementia Prevention and Intervention Platform
Schinle, M.; Dietrich, M.; Stock, S.; Gerdes, M.; Stork, W.
2023. Caring is Sharing. Ed.: M. Hägglund, 937–941, IOS Press. doi:10.3233/SHTI230313
End-to-End Deep Learning for Stress Recognition Using Remote Photoplethysmography
Zhou, K.; Schinle, M.; Weimar, S.; Gerdes, M.; Stock, S.; Stork, W.
2023. 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 1435–1442, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/BIBM55620.2022.9995577
Journal Articles
Digital Health Apps in the Context of Dementia: Questionnaire Study to Assess the Likelihood of Use Among Physicians
Schinle, M.; Erler, C.; Kaliciak, M.; Milde, C.; Stock, S.; Gerdes, M.; Stork, W.
2022. JMIR Formative Research, 6 (6), Art.-Nr.: e35961. doi:10.2196/35961
Journal Articles
Evaluation of Innovative SSVEP Stimulation Patterns for Neuro-Ophthalmology
Stock, S.; Gerdes, M.; Schinle, M.; Veloso de Oliveira, J.; Hauptmann, L.; Martini, L.; Stork, W.
2021. Investigative Ophthalmology & Visual Science, 62 (8), 2391–2391
Conference Papers
A Decision Process Model for De-Identification Methods on the Example of Psychometric Data
Schinle, M.; Erler, C.; Leenstra, S.; Stock, S.; Gerdes, M.; Stork, W.
2021. 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 1–6, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICECCME52200.2021.9591139
MMAI - Mobile Moods AI; Electroencephalography Artifact Detection; Towards Objective Assessment of Mental States
Stock, S.; Mazura, F.; De La Torre, F. G.; Gerdes, M.; Schinle, M.; Stork, W.
2021. 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 01–06, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICECCME52200.2021.9590972
Conference Papers
A system approach for closed-loop assessment of neuro-visual function based on convolutional neural network analysis of EEG signals
Stock, S. C.; Armengol-Urpi, A.; Kovács, B.; Maier, H.; Gerdes, M.; Stork, W.; Sarma, S. E.
2020. Online SPIE Photonics Europe, 6-10 April 2020. Neurophotonics. Vol.: 11360, 1136008/ 19 S., Society of Photo-optical Instrumentation Engineers (SPIE). doi:10.1117/12.2554417