research
During my doctoral studies, my research has primarily focused on detecting mental workload, stress, and emotions using wearable EEG sensors, both as a standalone and multi-modality with other physiological sensors. Building the foundation with healthy participants in lab and daily life settings, I was able to adapt the system to epilepsy patients to detect cognitive stress, with the goal of supporting self-management of stressful events that may precipitate seizures. In parallel, I developed algorithms to model and predict the optimal combination and dosage of anti-seizure medications (ASMs) to improve seizure prediction and personalized treatment strategies.
Research Interests:
- Affective Computing: Mental Workload, Emotion, Stress Detection
- Multimodal Wearable Sensors: EEG, PPG, EDA sensors
- Signal Processing
- Machine Learning, Deep Learning
- Epileptic Seizure Forecasting
- Non-Linear Time Series Algorithms: State Space Modeling, Kalman filter
teaching
Projects Supervision
| Winter Term 2021/2022 | Master Project | Human Emotion and Activity Classification Using Brain Activity Sensors |
| Winter Term 2020/2021 | Bachelor Project | Tracking behavior and eating habits using smart devices to support treatment of diabetes and obesity |
Thesis Supervision
| Master Thesis 2025 (ongoing) | Alaa Noor | Leveraging Transfer Learning for Epileptic Seizure Detection Using Stress-Induced EEG Signals |
| Bachelor Thesis 2024 | Jannis Hajda | Comparing Machine- and Deep-Learning Models for Generalized Binary Seizure Detection |
| Bachelor Thesis 2023 | Oleksandr Martemianov | Exploring Model Generalization with Streamlined Visualization for EEG-based Emotion Classification in Real Time |
| Master Thesis 2023 | Samik Real Enriquez | Deep Learning Multi-modal Mental Workload Classification -Experimental Evaluation of Modality Contribution |
| Master Thesis 2022 | Franziska Schumann | Online learning for EEG-based emotion classification |
publications
Moontaha, S., Cavalier, C., Esser, B., Jordan, A., Goebel, I., Anders, C., Mimi, A., Krüger, B., Surges, R., & Arnrich, B. (2025). EPIStress: A multimodal dataset of Physiological signals to measure cognitive stress in epilepsy patients. (under review)
Anders, C.* , Moontaha, S.* , Real, S., Stolp, F., & Arnrich, B. (2025, May). Multi-Modality Improves Cognitive Load Classification Of Naturalistic Tasks Under Varying Signal Quality. In Proceedings of the 19th EAI International Conference on Pervasive Computing Technologies for Healthcare. (In proceedings)
Anders, C.* , Moontaha, S.* , Real, S., & Arnrich, B. (2024). Unobtrusive measurement of cognitive load and physiological signals in uncontrolled environments. Scientific Data, 11(1), 1000. https://doi.org/10.1038/s41597-024-03738-7
Moontaha, S., Arnrich, B., & Galka, A. (2023). State Space Modeling of Event Count Time Series. Entropy, 25(10), 1372. https://doi.org/10.3390/e25101372
Kappattanavar, A. M., Hecker, P., Moontaha, S., Steckhan, N., & Arnrich, B. (2023). Food Choices after Cognitive Load: An Affective Computing Approach. Sensors, 23(14), 6597. https://doi.org/10.3390/s23146597
Moontaha, S.* , Schumann, F. E. F.* , & Arnrich, B. (2023). Online learning for wearable eeg-based emotion classification. Sensors, 23(5), 2387. https://doi.org/10.3390/s23052387
Moontaha, S., Kappattanavar, A. M., Hecker, P., & Arnrich, B. (2023). Wearable EEG-Based Cognitive Load Classification by Personalized and Generalized Model Using Brain Asymmetry. In HEALTHINF (pp. 41-51). https://doi.org/10.5220/0011628300003414
Hecker, P., Kappattanavar, A. M., Schmitt, M., Moontaha, S., Wagner, J., Eyben, F., ... & Arnrich, B. (2022, December). Quantifying cognitive load from voice using transformer-based models and a cross-dataset evaluation. In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 337-344). IEEE. https://doi.org/10.1109/ICMLA55696.2022.00055
Anders, C., Moontaha, S., Arnrich, B.(2022, October). Towards Multi-Modal Recordings in Daily Life: A Baseline Assessment of an Experimental Framework. Pervasive Health and Smart Sensing at the Information Society. http://library.ijs.si/Stacks/Proceedings/InformationSociety/2022/IS2022_Volume-H%20-%20PHSS.pdf
Moontaha, S., Steckhan, N., Kappattanavar, A., Surges, R., & Arnrich, B. (2020, May). Self-prediction of seizures in drug resistance epilepsy using digital phenotyping: a concept study. In Proceedings of the 14th EAI International Conference on Pervasive Computing Technologies for Healthcare (pp. 384-387). https://doi.org/10.1145/3421937.342194
Galka, A., Moontaha, S., & Siniatchkin, M. (2020). Constrained expectation maximisation algorithm for estimating ARMA models in state space representation. EURASIP Journal on Advances in Signal Processing, 2020(1), 1-37. https://doi.org/10.1186/s13634-020-00678-3
Moontaha, S., Galka, A., Siniatchkin, M., Scharlach, S., von Spiczak, S., Stephani, U., May, T., & Meurer, T. (2019, July). SVD square-root iterated extended Kalman filter for modeling of epileptic seizure count time series with external inputs. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 616-619). IEEE. http://doi.org/10.1109/EMBC.2019.8857159
Moontaha, S., Galka, A., Meurer, T., & Siniatchkin, M. (2018, July). Analysis of the effects of medication for the treatment of epilepsy by ensemble Iterative Extended Kalman filtering. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 187-190). IEEE. http://doi.org/10.1109/EMBC.2018.8512179
* indicating shared first authorship
personal
November 2019 - Ongoing: PhD Candidate, Chair Digital Health - Connected Healthcare, Digital Health Cluster, Hasso Plattner Institute, Potsdam, Germany
February 2016 - October 2019: Guest Researcher (Forschungsaufenthalt ), Chair of Automatic Control, Department of Medical Psychology and Medical Sociology, Kiel University, Germany.
March 2015 - January 2016: Guest Researcher (Forschungsaufenthalt ), Department of Medical Psychology and Medical Sociology, Kiel University, Germany.
October 2013 - January 2016: Master in Science (MSc) in Digital Communications, Kiel University, Germany
May 2008 - January 2012: Bachelor in Science (BSc) in Electrical and Electronic Engineering, American International University-Bangladesh, Bangladesh