Prof. Dr.-Ing. Bert Arnrich

Sidratul Moontaha

Research Assistant, PhD Candidate

Phone: +49-(0)331 5509-3481

Room: G-2.1.21

Email: sidratul.moontaha(at)hpi.de


Personal Information

  • 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

Research Interests

  • Affective Computing: Mental Workload, Emotion
  • Multimodal Wearable Sensors: Electroencephalography (EEG), Photoplethysmography (PPG), and Electrodermal Activity (EDA)  sensors
  • Signal Processing
  • Machine Learning, Deep Learning
  • Epileptic Seizure Prediction
  • Multivariate Time Series Analysis
  • State Space Modeliing
  • Non-Linear Time Series Algorithms: Kalman filter
  • Optimization Algorithms: EM Algorithm


In my doctoral thesis, I perform affective computing with multimodal sensors: Electroencephalography (EEG), Photoplethysmography (PPG), and Electrodermal Activity (EDA)  sensors. The goal is to predict human affects in terms of mental workload, stress, and emotion in a controlled setup and a daily life environment. The motivation is to provide objective biofeedback to epilepsy patients of an upcoming seizure by observing their affectivity. Moreover, within my research work, I analyze the medication dosages given to epilepsy patients to predict seizures. The following section lists all projects that have been completed or ongoing to date during my doctoral studies.


Quantifying Mental Workload and Stress in Everyday Situations

Mental workload is a term from cognitive psychology which refers to the amount of working memory used in the brain. The ratio of the occupied processing capability of the working memory and the amount required by the task can be referred to as mental workload. Therefore, identifying a potential mental overload is essential, especially for drivers, pilots, medical professionals, emergency workers, and air traffic control professionals. Moreover, complex cognitive tasks alone or combined with other factors like time or social pressure can release cortisol resulting in psychological stress, a primary premonitory symptom of an epileptic seizure. Therefore, this project aims to predict the mental workload to provide pre-emptive therapy for epilepsy patients.

This project was started by curating EEG data from 11 healthy participants while performing mental workload and relaxation tasks in a controlled environment. The recent development of wearable EEG devices made data collection convenient through fewer electrodes. Frequency domain features were extracted from raw EEG data after pre-processing using outlier rejection based on a movement filter, spectral filtering, common average referencing, and normalization. An exploratory feature analysis shows that brain asymmetry features are the most important to predict mental workload. After that, the data collection is extended to include multimodality i.e., PPG and EDA sensors and daily life data collection along with the controlled environment data. The data collection and recruitment is ongoing with a successful data collection from 20 participants. The research question of how reliably mental workload can be predicted mainly in daily life with providing information on the important modalities is yet to be answered from this project. 

Data analysis pipeline for mental workload classification

Online Learning for Wearable EEG-based Emotion Classification

Giving emotional intelligence to the machines could, for instance, facilitate earlydetection and prediction of (mental) diseases and symptoms. Therefore, in this project, objective prediction of emotional states in real-time has been provided by recording Electroencephalography (EEG) data. The real-time emotion classification pipeline trains different binary classifiers for the dimensions of Valence and Arousal from an incoming EEG data stream coming from the state-of-art AMIGOS dataset and the curated dataset within this project. The pipeline outperformed the related work with the immediate label setting and tested for live scenario where the labels arrive after a certain delay. This prototype on the healthy participants can be used to assess the emotional bias of the patient cohort in realtime.The results of this project were submitted at the MDPI (Sensors) journal. As potential future works, we plan to include multimodal sensors, incorporate bio-feedback, and increase the cohort size of the curated dataset. 

Emotion prediction from EEG data stream

State Space Modelling of Event Count Time Series

This project develops an algorithm for analysing event count time series by application of non-linear state space modelling and Kalman filtering. The algorithm is applied to time series of the daily number of seizures of drug-resistant epilepsy patients. This number may depend on dosages of simultaneously administered anti-epileptic drugs, their superposition effects, delay effects, and unknown factors, making the objective analysis of seizure count time series arduous. In order to estimate the states of the non-linear state space model, an iterative extended Kalman filter is employed. Positive definiteness of covariance matrices is preserved by a square-root filtering approach based on Singular Value Decomposition. Non-negativity of the count data is ensured, either by an exponential observation function, or by a newly introduced “affinely distorted hyperbolic” observation function. The resulting algorithm has to be validated on simulated data by deciding whether a particular anti-epileptic drug is increasing or reducing the seizure rate. The algorithm has also to be applied to clinical data from patients suffering from Myoclonic Astatic Epilepsy. The decision on the increase or decrease of seizure counts is then validated by statistical testing and by visual assessment by experienced pediatric epileptologists.


Teaching Activities

I am open for new ideas for Master's theses which are related to my research interests.



  • Food Choices after Cognitive Load: An Affective Computing Approach. Kappattanavar, Arpita Mallikarjuna; Hecker, Pascal; Moontaha, Sidratul; Steckhan, Nico; Arnrich, Bert in Sensors (2023). 23(14)
  • Wearable EEG-Based Cognitive Load Classification by Personalized and Generalized Model Using Brain Asymmetry. Moontaha, Sidratul; Kappattanavar, Arpita Mallikarjuna; Hecker, Pascal; Arnrich, Bert (2023).
  • Online learning for wearable eeg-based emotion classification. Moontaha, Sidratul; Schumann, Franziska Elisabeth Friederike; Arnrich, Bert in Sensors (2023). 23(5) 2387.


  • Towards Multi-Modal Recordings in Daily Life: A Baseline Assessment of an Experimental Framework Anders, Christoph; Moontaha, Sidratul; Arnrich, Bert in IS (2022). (Vol. H) 27–30. Information Society.
  • Quantifying Cognitive Load from Voice using Transformer-Based Models and a Cross-Dataset Evaluation. Hecker, Pascal; Kappattanavar, Arpita M.; Schmitt, Maximilian; Moontaha, Sidratul; Wagner, Johannes; Eyben, Florian; Schuller, Björn W.; Arnrich, Bert (2022). 337–344.
  • Towards Multi-Modal Recordings in Daily Life: A Baseline Assessment of an Experimental Frame- work. Moontaha, Sidratul; Anders, Christoph; Arnrich, Bert (2022). 27–30.
  • Quantifying Cognitive Load from Voice using Transformer-Based Models and a Cross-Dataset Evaluation. Hecker, Pascal; Kappattanavar, Arpita M; Schmitt, Maximilian; Moontaha, Sidratul; Wagner, Johannes; Eyben, Florian; Schuller, Björn W; Arnrich, Bert (2022). 337–344.


  • Self-prediction of seizur... - Download
    Self-prediction of seizures in drug resistance epilepsy using digital phenotyping: a concept study. Moontaha, Sidratul; Steckhan, Nico; Kappattanavar, Arpita; Surges, Rainer; Arnrich, Bert (2020). (Vol. 14)
  • Constrained expectation m... - Download
    Constrained expectation maximisation algorithm for estimating ARMA models in state space representation. Galka, Andreas; Moontaha, Sidratul; SIniatchkin, Siniatchkin in EURASIP Journal on Advances in Signal Processing 2020.1 (2020). 1–37.


  • Bewertung von Therapieeff... - Download
    Bewertung von Therapieeffekten bei Epilepsie: Eine vergleichende Analyse zwischen Cox-Stuart-Berechnung und Zustandsraum-Modellierung Scharlach, Sascha; Moontaha, Sirdatul; von Spiczak, Sarah; Stephani, Ulrich; Siniatchkin, Michael; May, Theodor; Galka, Andreas; Meurer, Thomas (2019).
  • SVD Square-root Iterated ... - Download
    SVD Square-root Iterated Extended Kalman Filter for Modeling of Epileptic Seizure Count Time Series with External Inputs. Moontaha, Sidratul; Galka, Andreas; Siniatchkin, Michael; Scharlach, Sascha; von Spiczak, Sarah; Stephani, Ulrich; May, Theodor; Meurer, Thomas (2019). (Vol. 41) 616–619.


  • Analysis of the effects o... - Download
    Analysis of the effects of medication for the treatment of epilepsy by ensemble Iterative Extended Kalman Filtering. Moontaha, Sidratul; Galka, Andreas; Meurer, Thomas; Siniatchkin, Michael (2018). (Vol. 40) 187–190.