Translational Neuromodeling Unit
I received my bachelor’s degree in engineering sciences from the Technical University of Munich (TUM) in 2018 and passed the elite master’s degree in neuroengineering (TUM) with honours in 2022. Between 2018 and 2021 I was employed at the Institute for Cognitive Systems (TUM) where I worked on adaptive machine learning algorithms for the decoding of neuronal signals in brain–computer interfaces. In my master thesis I worked on interpretable machine learning methods in hearing diagnostics using Bayesian Inference in a joint project between the University of Tübingen, the Harvard Medical School and the TUM. Since 2022 I am part of the Translational Neuromodeling Unit where I focus on the development of mechanistic models of the insula cortex as well as analysis of EEG and OPM data.
Schönleitner, F. M., Otter, L., Ehrlich, S. K., & Cheng, G. (2020). Calibration-free error-related potential decoding with adaptive subject-independent models: a comparative study. IEEE Transactions on Medical Robotics and Bionics, 2(3), 399-409.
Schönleitner, F. M., Otter, L., Ehrlich, S. K., & Cheng, G. (2019, September). A comparative study on adaptive subject-independent classification models for zero-calibration error-potential decoding. In 2019 IEEE International Conference on Cyborg and Bionic Systems (CBS) (pp. 85-90). IEEE.