2026
Gfesser, Torsten; Witte, Thomas; Krüger, Björn
From Groups to Individuals: Generalization Challenges of HRV Based Classifiers Proceedings Article Forthcoming
In: HCI International 2026, Springer, Forthcoming.
@inproceedings{gfesser2026a,
title = {From Groups to Individuals: Generalization Challenges of HRV Based Classifiers},
author = {Torsten Gfesser and Thomas Witte and Björn Krüger},
year = {2026},
date = {2026-07-31},
urldate = {2026-07-31},
booktitle = {HCI International 2026},
publisher = {Springer},
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pubstate = {forthcoming},
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Gfesser, Torsten; Witte, Thomas; Krüger, Björn
On the Efficacy and Usability of Adaptive Instructional Systems Proceedings Article Forthcoming
In: HCI International 2026, Springer, Forthcoming.
@inproceedings{nokey,
title = {On the Efficacy and Usability of Adaptive Instructional Systems},
author = {Torsten Gfesser and Thomas Witte and Björn Krüger},
year = {2026},
date = {2026-07-31},
urldate = {2026-07-31},
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Kretschmer-Trendowicz, Anett; Moser, Florian; Gürster, Lena; Pippirs, Corinna; Maas, Pia; Zeiler, Anne; Steininger, Melissa; Walk, Simon; von Bock, Christian; Krüger, Björn; Spittler, Thomas
Virtual Interaction to Promote Mental Health in Children with Social Anxiety Disorders (VISAKI) Conference Forthcoming
European Congress of Psychiatry 2026, Forthcoming.
@conference{kretschmer2026a,
title = {Virtual Interaction to Promote Mental Health in Children with Social Anxiety Disorders (VISAKI)},
author = {Anett Kretschmer-Trendowicz and Florian Moser and Lena Gürster and Corinna Pippirs and Pia Maas and Anne Zeiler and Melissa Steininger and Simon Walk and Christian von Bock and Björn Krüger and Thomas Spittler},
year = {2026},
date = {2026-04-01},
booktitle = {European Congress of Psychiatry 2026},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
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Steininger, Melissa; Jansen, Anna; Mustafa, Sarah Al-Haj; Bouzan, Nataly; Surges, Rainer; Helmstaedter, Christoph; von Wrede, Randi; Krüger, Björn
Linking Higher-level Eye Tracking Metrics to High-Impact Antiseizure Medication in Epilepsy Patients Conference Forthcoming
4th International Conference on Artificial Intelligence in Epilepsy and Neurological Disorders, Forthcoming.
@conference{steininger2026a,
title = {Linking Higher-level Eye Tracking Metrics to High-Impact Antiseizure Medication in Epilepsy Patients},
author = {Melissa Steininger and Anna Jansen and Sarah Al-Haj Mustafa and Nataly Bouzan and Rainer Surges and Christoph Helmstaedter and Randi von Wrede and Björn Krüger},
year = {2026},
date = {2026-03-31},
booktitle = {4th International Conference on Artificial Intelligence in Epilepsy and Neurological Disorders},
keywords = {},
pubstate = {forthcoming},
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Jansen, Anna; Waldow, Kristoffer; Pötter, Sebastian; Civelek, Turhan; Steininger, Melissa; Perret, Jerome; Wellmann, Markus; Stein, Steffen-Sascha; Lähner, David; Welle, Kristian; Fuhrmann, Arnulph; Krüger, Björn
VIRTOSHA - A VR Training Simulation for Osteosynthesis Procedures with Force Feedback and Tissue Simulation Proceedings Article Forthcoming
In: IEEE VR 2026 Workshop: XR-MED, Forthcoming.
@inproceedings{jansen2026b,
title = {VIRTOSHA - A VR Training Simulation for Osteosynthesis Procedures with Force Feedback and Tissue Simulation},
author = {Anna Jansen and Kristoffer Waldow and Sebastian Pötter and Turhan Civelek and Melissa Steininger and Jerome Perret and Markus Wellmann and Steffen-Sascha Stein and David Lähner and Kristian Welle and Arnulph Fuhrmann and Björn Krüger},
year = {2026},
date = {2026-03-31},
urldate = {2026-03-31},
booktitle = {IEEE VR 2026 Workshop: XR-MED},
abstract = {Osteosynthesis training requires development of force-sensitive manual skills and an understanding of workflows, which are difficult to acquire through theoretical instruction or cadaver-based training. While Virtual Reality (VR) offers new opportunities for surgical training, existing systems often focus on isolated subtasks, lacking integrated support for realistic interaction, procedural logic, and adaptability. This paper presents a work-in-progress VR training system designed for workflow-oriented osteosynthesis training. The system combines force feedback, physics-based tissue simulation and robust hand tracking in a modular architecture. Additionally, an expert-driven authoring workflow enables medical professionals to define and adapt training scenarios without programming.
Using a reference scenario for fibular fracture osteosynthesis, we describe the system design, core components, and current implementation status. We further discuss technical trade-offs, limitations, and directions for future validation. Our system establishes a foundation for force-sensitive, workflow-oriented VR training and serves as a basis for future studies in surgical education.},
keywords = {},
pubstate = {forthcoming},
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Osteosynthesis training requires development of force-sensitive manual skills and an understanding of workflows, which are difficult to acquire through theoretical instruction or cadaver-based training. While Virtual Reality (VR) offers new opportunities for surgical training, existing systems often focus on isolated subtasks, lacking integrated support for realistic interaction, procedural logic, and adaptability. This paper presents a work-in-progress VR training system designed for workflow-oriented osteosynthesis training. The system combines force feedback, physics-based tissue simulation and robust hand tracking in a modular architecture. Additionally, an expert-driven authoring workflow enables medical professionals to define and adapt training scenarios without programming.
Using a reference scenario for fibular fracture osteosynthesis, we describe the system design, core components, and current implementation status. We further discuss technical trade-offs, limitations, and directions for future validation. Our system establishes a foundation for force-sensitive, workflow-oriented VR training and serves as a basis for future studies in surgical education. Steininger, Melissa; Jansen, Anna; Müllers, Johannes; von Wrede, Randi; Krüger, Björn
Toward Interpretable Cognitive Screening in Epilepsy: Eye Tracking in a VR Trail Making Test Proceedings Article Forthcoming
In: IEEE VR 2026 Workshop: GEMINI, Forthcoming.
@inproceedings{steininger2026b,
title = {Toward Interpretable Cognitive Screening in Epilepsy: Eye Tracking in a VR Trail Making Test},
author = {Melissa Steininger and Anna Jansen and Johannes Müllers and Randi von Wrede and Björn Krüger},
year = {2026},
date = {2026-03-31},
urldate = {2026-03-31},
booktitle = {IEEE VR 2026 Workshop: GEMINI},
abstract = {Cognitive screening is a routine component of epilepsy care. Established pen-and-paper instruments such as the Trail Making Test (TMT) primarily yield summary outcomes (e.g., completion time) that provide limited insight into visual search and executive-control processes affected by epilepsy-related brain network dysfunction. We present an eye-tracked Virtual Reality TMT (VR-TMT) as a controlled research instrument that enables process-level interpretable measurements. The system synchronizes continuous eye-movement streams with timestamped task events (task start/stop and node selections) and logs gaze-to-Area-of-Interest (AOI) intersections. To reduce VR-specific confounds that can compromise cognitive interpretation, we specify concrete design guidelines for 3D stimulus geometry and the VR+eye-tracking setup (e.g., viewing distance, field-of-view placement, target size).
In a feasibility pilot (n=8) usability ratings were favorable and cybersickness was low. Building on this foundation, we outline an analysis framework that derives contextualized gaze features and evaluates their added value in explaining established cognitive screening outcomes in epilepsy cohorts.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
Cognitive screening is a routine component of epilepsy care. Established pen-and-paper instruments such as the Trail Making Test (TMT) primarily yield summary outcomes (e.g., completion time) that provide limited insight into visual search and executive-control processes affected by epilepsy-related brain network dysfunction. We present an eye-tracked Virtual Reality TMT (VR-TMT) as a controlled research instrument that enables process-level interpretable measurements. The system synchronizes continuous eye-movement streams with timestamped task events (task start/stop and node selections) and logs gaze-to-Area-of-Interest (AOI) intersections. To reduce VR-specific confounds that can compromise cognitive interpretation, we specify concrete design guidelines for 3D stimulus geometry and the VR+eye-tracking setup (e.g., viewing distance, field-of-view placement, target size).
In a feasibility pilot (n=8) usability ratings were favorable and cybersickness was low. Building on this foundation, we outline an analysis framework that derives contextualized gaze features and evaluates their added value in explaining established cognitive screening outcomes in epilepsy cohorts. Jansen, Anna; Steininger, Melissa; Mustafa, Sarah Al-Haj; Bouzan, Nataly; Surges, Rainer; Helmstaedter, Christoph; von Wrede, Randi; Krüger, Björn
Higher-Level Eye Tracking Metrics Reveal Search Behaviour Differences in Persons with Epilepsy vs. Healthy Controls Conference Forthcoming
4th International Conference on Artificial Intelligence in Epilepsy and Neurological Disorders, Forthcoming.
@conference{jansen2026a,
title = {Higher-Level Eye Tracking Metrics Reveal Search Behaviour Differences in Persons with Epilepsy vs. Healthy Controls},
author = {Anna Jansen and Melissa Steininger and Sarah Al-Haj Mustafa and Nataly Bouzan and Rainer Surges and Christoph Helmstaedter and Randi von Wrede and Björn Krüger},
year = {2026},
date = {2026-03-30},
booktitle = {4th International Conference on Artificial Intelligence in Epilepsy and Neurological Disorders},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
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Müllers, Johannes; Siddiquie, Usama; Lemken, Johannes; Staehle, Ricarda; Schulte-Rüther, Martin; Krüger, Björn
MARVEL: A Human-in-the-Loop Web Platform for Multimodal Annotation and Classification of Social Behavior Conference Forthcoming
17th Autism Spectrum Scientific Conference, Forthcoming.
@conference{Muellers2026,
title = {MARVEL: A Human-in-the-Loop Web Platform for Multimodal Annotation and Classification of Social Behavior},
author = {Johannes Müllers and Usama Siddiquie and Johannes Lemken and Ricarda Staehle and Martin Schulte-Rüther and Björn Krüger},
year = {2026},
date = {2026-03-14},
urldate = {2026-03-14},
booktitle = {17th Autism Spectrum Scientific Conference},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
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Staehle, Ricarda; Siddiquie, Usama; Müllers, Johannes; Krüger, Björn; Poustka, Luise; Schulte-Rüther, Martin
Clinical Annotation of Socio-Emotional Signals in Autism: Facilitating Diagnostic Review, Consensus Building, and Machine Learning Applications Conference Forthcoming
17th Autism Spectrum Scientific Conference, Forthcoming.
@conference{nokey,
title = {Clinical Annotation of Socio-Emotional Signals in Autism: Facilitating Diagnostic Review, Consensus Building, and Machine Learning Applications},
author = {Ricarda Staehle and Usama Siddiquie and Johannes Müllers and Björn Krüger and Luise Poustka and Martin Schulte-Rüther},
year = {2026},
date = {2026-03-14},
booktitle = {17th Autism Spectrum Scientific Conference},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
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Goharinejad, Saeideh; Goharinezhad, Salime; Moulaei, Khadijeh; Krüger, Björn; Spittler, Thomas
In: INQUIRY: The Journal of Health Care Organization, Provision, and Financing, vol. 63, pp. 00469580251413101, 2026.
@article{Goharinejad-2025,
title = {Assessing the Impact of Virtual Reality, Augmented Reality, and Video Games on Improving Post-Traumatic Stress Disorder Symptoms: A Systematic Review and Meta-Analysis},
author = {Saeideh Goharinejad and Salime Goharinezhad and Khadijeh Moulaei and Björn Krüger and Thomas Spittler},
url = {https://doi.org/10.1177/00469580251413101},
doi = {10.1177/00469580251413101},
year = {2026},
date = {2026-01-28},
urldate = {2025-12-01},
journal = {INQUIRY: The Journal of Health Care Organization, Provision, and Financing},
volume = {63},
pages = {00469580251413101},
abstract = {Post-traumatic stress disorder (PTSD) is often debilitating, with current treatments limited by low adherence, high costs, and accessibility issues. Innovative technologies such as virtual reality (VR), augmented reality (AR), and therapeutic video games provide immersive environments that may improve treatment outcomes. This systematic review and meta-analysis evaluated the efficacy of these approaches and explored their potential advantages over traditional methods. A comprehensive search of PubMed, PsycINFO, CINAHL, Web of Science, and Cochrane identified relevant studies. Two reviewers independently screened articles, extracted data, and assessed quality using the Mixed Methods Appraisal Tool (MMAT). A random-effects model was used to calculate pooled effect sizes (Hedges’ g), and heterogeneity was evaluated with the Q test and I2 statistic. Publication bias was examined with funnel plots, Egger’s, and Begg’s tests. Analyses were performed in Stata version 17.0. From 480 records, 21 studies were included in the review and 12 in the meta-analysis. VR-based treatments yielded a pooled effect size of –0.35 (95% CI [–0.57, –0.13]), indicating a small-to-moderate reduction in PTSD symptoms. The effect was statistically significant (z = –3.13, P < .01), with moderate heterogeneity (I2 = 46.28%, P = .03). Funnel plots and statistical tests suggested minimal publication bias. Meta-regression showed no moderating effect of gender. Subgroup analyses indicated significant benefits in male-only samples, participants aged 20 to 30 and over 40, and studies with follow-up periods ≤7 months. Larger effects were observed in studies with 15 to 30 participants. VR, AR, and video game interventions significantly reduce PTSD symptoms and may enhance accessibility and engagement compared to traditional treatments. These findings support the integration of immersive technologies into therapeutic practice to improve outcomes for individuals with PTSD. }
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Post-traumatic stress disorder (PTSD) is often debilitating, with current treatments limited by low adherence, high costs, and accessibility issues. Innovative technologies such as virtual reality (VR), augmented reality (AR), and therapeutic video games provide immersive environments that may improve treatment outcomes. This systematic review and meta-analysis evaluated the efficacy of these approaches and explored their potential advantages over traditional methods. A comprehensive search of PubMed, PsycINFO, CINAHL, Web of Science, and Cochrane identified relevant studies. Two reviewers independently screened articles, extracted data, and assessed quality using the Mixed Methods Appraisal Tool (MMAT). A random-effects model was used to calculate pooled effect sizes (Hedges’ g), and heterogeneity was evaluated with the Q test and I2 statistic. Publication bias was examined with funnel plots, Egger’s, and Begg’s tests. Analyses were performed in Stata version 17.0. From 480 records, 21 studies were included in the review and 12 in the meta-analysis. VR-based treatments yielded a pooled effect size of –0.35 (95% CI [–0.57, –0.13]), indicating a small-to-moderate reduction in PTSD symptoms. The effect was statistically significant (z = –3.13, P < .01), with moderate heterogeneity (I2 = 46.28%, P = .03). Funnel plots and statistical tests suggested minimal publication bias. Meta-regression showed no moderating effect of gender. Subgroup analyses indicated significant benefits in male-only samples, participants aged 20 to 30 and over 40, and studies with follow-up periods ≤7 months. Larger effects were observed in studies with 15 to 30 participants. VR, AR, and video game interventions significantly reduce PTSD symptoms and may enhance accessibility and engagement compared to traditional treatments. These findings support the integration of immersive technologies into therapeutic practice to improve outcomes for individuals with PTSD. }
2025
Bhatti, Faraz Ahmad; Riaz, Qaiser; Krüger, Björn
Beyond Falls: A Hybrid CNN–LSTM–Attention Framework for Pre-, Transition-, and Post-Fall Detection with Wearable Inertial Sensors Journal Article
In: IEEE Access, 2025.
@article{Bhatti2025,
title = {Beyond Falls: A Hybrid CNN–LSTM–Attention Framework for Pre-, Transition-, and Post-Fall Detection with Wearable Inertial Sensors},
author = {Faraz Ahmad Bhatti and Qaiser Riaz and Björn Krüger},
doi = {10.1109/ACCESS.2025.3641198},
year = {2025},
date = {2025-12-05},
urldate = {2025-12-02},
journal = {IEEE Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Barzegar, Mohammad Mehdi; Daryakenari, Nazila Ahmadi; Khodatars, Marjane
Explainable Epileptic Seizure Detection from Electroencephalography Signals via CNN–Bi-LSTM Attention Hybrid Model Journal Article
In: Journal of Research and Health, vol. 15, no. 6, 2025.
@article{Barzegar2025,
title = {Explainable Epileptic Seizure Detection from Electroencephalography Signals via CNN–Bi-LSTM Attention Hybrid Model},
author = {Mohammad Mehdi Barzegar and Nazila Ahmadi Daryakenari and Marjane Khodatars},
url = {http://jrh.gmu.ac.ir/article-1-2987-en.html},
doi = {10.32598/JRH.15.SP.2892.1},
year = {2025},
date = {2025-12-01},
urldate = {2025-12-01},
journal = {Journal of Research and Health},
volume = {15},
number = {6},
abstract = {Background: Epilepsy is a chronic neurological disorder marked by recurrent daily seizures that threaten patient safety. Electroencephalography (EEG) is a crucial neuroimaging tool for epilepsy diagnosis, but manual interpretation of EEG signals is challenging for clinicians. To assist specialists, automated systems, such as computer-aided diagnosis systems (CADS) based on deep learning (DL) are essential. Methods: The proposed CADS system was validated using the Turkish epilepsy dataset. In preprocessing, EEG signals were filtered, down-sampled, re-referenced using common average reference (CAR), and segmented into multiple temporal windows. A new feature extraction framework combining one-dimensional convolutional neural networks (1D-CNN), bidirectional long short-term memory (Bi-LSTM), and an attention mechanism was developed. All experiments were performed using 5-fold cross-validation. Post-hoc explainability was evaluated using explainable artificial intelligence (XAI) techniques, including t-distributed stochastic neighbor embedding (t-SNE) and shapley additive explanations (SHAP). Results: The proposed CADS achieved a seizure diagnosis accuracy of 99.49%, demonstrating high robustness across the validation folds, with minimal variance between folds (±0.12%). Feature space visualization confirmed clear class separation, and SHAP analysis provided clinically meaningful explanations for model decisions. Conclusion: The proposed DL architecture shows strong potential for reliable and interpretable automatic epileptic seizure detection from EEG. This CADS can significantly reduce the diagnostic burden on clinicians and support real-time decision-making in clinical environments.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: Epilepsy is a chronic neurological disorder marked by recurrent daily seizures that threaten patient safety. Electroencephalography (EEG) is a crucial neuroimaging tool for epilepsy diagnosis, but manual interpretation of EEG signals is challenging for clinicians. To assist specialists, automated systems, such as computer-aided diagnosis systems (CADS) based on deep learning (DL) are essential. Methods: The proposed CADS system was validated using the Turkish epilepsy dataset. In preprocessing, EEG signals were filtered, down-sampled, re-referenced using common average reference (CAR), and segmented into multiple temporal windows. A new feature extraction framework combining one-dimensional convolutional neural networks (1D-CNN), bidirectional long short-term memory (Bi-LSTM), and an attention mechanism was developed. All experiments were performed using 5-fold cross-validation. Post-hoc explainability was evaluated using explainable artificial intelligence (XAI) techniques, including t-distributed stochastic neighbor embedding (t-SNE) and shapley additive explanations (SHAP). Results: The proposed CADS achieved a seizure diagnosis accuracy of 99.49%, demonstrating high robustness across the validation folds, with minimal variance between folds (±0.12%). Feature space visualization confirmed clear class separation, and SHAP analysis provided clinically meaningful explanations for model decisions. Conclusion: The proposed DL architecture shows strong potential for reliable and interpretable automatic epileptic seizure detection from EEG. This CADS can significantly reduce the diagnostic burden on clinicians and support real-time decision-making in clinical environments. Moontaha, Sidratul; Cavalier, Constanze; Esser, Birgitta; Jordan, Arthur; Goebel, Ines; Anders, Christoph; Mimi, Afsana; Krüger, Björn; Surges, Rainer; Arnrich, Bert
EPIStress: A multimodal dataset of Physiological signals to measure cognitive stress in epilepsy patients Journal Article
In: Scientific Data, vol. 12, iss. 1, no. 1867, 2025, ISBN: 2052-4463.
@article{Moontaha2025,
title = {EPIStress: A multimodal dataset of Physiological signals to measure cognitive stress in epilepsy patients},
author = {Sidratul Moontaha and Constanze Cavalier and Birgitta Esser and Arthur Jordan and Ines Goebel and Christoph Anders and Afsana Mimi and Björn Krüger and Rainer Surges and Bert Arnrich},
url = {https://doi.org/10.1038/s41597-025-06328-3},
doi = {10.1038/s41597-025-06328-3},
isbn = {2052-4463},
year = {2025},
date = {2025-11-28},
urldate = {2025-12-01},
journal = {Scientific Data},
volume = {12},
number = {1867},
issue = {1},
abstract = {Epilepsy patients commonly report stress as a frequent seizure trigger; however, the objective seizure-stress relationship is unclear due to self-report biases and difficulty in objective quantification of stress. This work presents a dataset from twenty epilepsy patients undergoing cognitive stress elicitation protocols, participating in laboratory experiments with computer-based tasks at predefined difficulty levels, and in situational experiments by independently choosing tasks with at least two difficulty levels. Physiological signals from wearable electroencephalography, photoplethysmography, acceleration, electrodermal activity, and temperature sensors were recorded. The task-related perceived cognitive stress was collected using two 5-point Likert scales of self-reported mental workload and stress, contrasted by a pairwise NASA-TLX questionnaire. Additionally, the dataset includes a patient-reported list of seizure-provoking and -inhibiting factors. Results illustrated individual and heterogeneous responses to cognitive tasks, with some modalities yielding statistically significant features, while others demonstrated expected directional trends. The findings support the validity and suitability of the proposed dataset for cognitive stress detection and the potential to map seizure-related factors to cognitive stress events.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Epilepsy patients commonly report stress as a frequent seizure trigger; however, the objective seizure-stress relationship is unclear due to self-report biases and difficulty in objective quantification of stress. This work presents a dataset from twenty epilepsy patients undergoing cognitive stress elicitation protocols, participating in laboratory experiments with computer-based tasks at predefined difficulty levels, and in situational experiments by independently choosing tasks with at least two difficulty levels. Physiological signals from wearable electroencephalography, photoplethysmography, acceleration, electrodermal activity, and temperature sensors were recorded. The task-related perceived cognitive stress was collected using two 5-point Likert scales of self-reported mental workload and stress, contrasted by a pairwise NASA-TLX questionnaire. Additionally, the dataset includes a patient-reported list of seizure-provoking and -inhibiting factors. Results illustrated individual and heterogeneous responses to cognitive tasks, with some modalities yielding statistically significant features, while others demonstrated expected directional trends. The findings support the validity and suitability of the proposed dataset for cognitive stress detection and the potential to map seizure-related factors to cognitive stress events. Steininger, Melissa; Marquardt, Alexander; Perusquía-Hernández, Monica; Lehnort, Marvin; Otsubo, Hiromu; Dollack, Felix; Kruijff, Ernst; Krüger, Björn; Kiyokawa, Kiyoshi; Riecke, Bernhard E.
The Awe-some Spectrum: Self-Reported Awe Varies by Eliciting Scenery and Presence in Virtual Reality, and the User's Nationality Proceedings Article
In: 2025 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 1267-1277, 2025.
@inproceedings{steininger2025c,
title = {The Awe-some Spectrum: Self-Reported Awe Varies by Eliciting Scenery and Presence in Virtual Reality, and the User's Nationality},
author = {Melissa Steininger and Alexander Marquardt and Monica Perusquía-Hernández and Marvin Lehnort and Hiromu Otsubo and Felix Dollack and Ernst Kruijff and Björn Krüger and Kiyoshi Kiyokawa and Bernhard E. Riecke
},
doi = {10.1109/ISMAR67309.2025.00132},
year = {2025},
date = {2025-11-11},
urldate = {2025-10-01},
booktitle = {2025 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)},
pages = {1267-1277},
abstract = {Awe is a multifaceted emotion often associated with the perception of vastness, that challenges existing mental frameworks. Despite its growing relevance in affective computing and psychological research, awe remains difficult to elicit and measure.
This raises the research questions of how awe can be effectively elicited, which factors are associated with the experience of awe, and whether it can reliably be measured using biosensors.
For this study, we designed ten immersive Virtual Reality (VR) scenes with dynamic transitions from narrow to vast environments. These scenes were used to explore how awe relates to environmental features (abstract, human-made, nature), personality traits, and country of origin. We collected skin conductance, respiration data, and self-reported awe and presence from participants from Germany, Japan, and Jordan.
Our results indicate that self-reported awe varies significantly across countries and scene types. In particular, a scene depicting outer space elicited the strongest awe. Scenes that elicited high self-reported awe also induced a stronger sense of presence. However, we found no evidence that awe ratings are correlated with physiological responses.
These findings challenge the assumption that awe is reliably reflected in autonomic arousal and underscore the importance of cultural and perceptual context.
Our study offers new insights into how immersive VR can be designed to elicit awe, and suggests that subjective reports—rather than physiological signals—remain the most consistent indicators of emotional impact.},
keywords = {},
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tppubtype = {inproceedings}
}
Awe is a multifaceted emotion often associated with the perception of vastness, that challenges existing mental frameworks. Despite its growing relevance in affective computing and psychological research, awe remains difficult to elicit and measure.
This raises the research questions of how awe can be effectively elicited, which factors are associated with the experience of awe, and whether it can reliably be measured using biosensors.
For this study, we designed ten immersive Virtual Reality (VR) scenes with dynamic transitions from narrow to vast environments. These scenes were used to explore how awe relates to environmental features (abstract, human-made, nature), personality traits, and country of origin. We collected skin conductance, respiration data, and self-reported awe and presence from participants from Germany, Japan, and Jordan.
Our results indicate that self-reported awe varies significantly across countries and scene types. In particular, a scene depicting outer space elicited the strongest awe. Scenes that elicited high self-reported awe also induced a stronger sense of presence. However, we found no evidence that awe ratings are correlated with physiological responses.
These findings challenge the assumption that awe is reliably reflected in autonomic arousal and underscore the importance of cultural and perceptual context.
Our study offers new insights into how immersive VR can be designed to elicit awe, and suggests that subjective reports—rather than physiological signals—remain the most consistent indicators of emotional impact. Jansen, Anna; Morev, Nikita; Steininger, Melissa; Müllers, Johannes; Krüger, Björn
Synthetic Hand Dataset Generation: Multi-View Rendering and Annotation with Blender Proceedings Article
In: 2025 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), pp. 809-810, IEEE Computer Society, 2025.
@inproceedings{jansen2025c,
title = {Synthetic Hand Dataset Generation: Multi-View Rendering and Annotation with Blender},
author = {Anna Jansen and Nikita Morev and Melissa Steininger and Johannes Müllers and Björn Krüger},
url = {https://www.computer.org/csdl/proceedings-article/ismar-adjunct/2025/934700a809/2bKcNnpvzTG},
doi = {10.1109/ISMAR-Adjunct68609.2025.00201},
year = {2025},
date = {2025-10-06},
urldate = {2025-10-06},
booktitle = {2025 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)},
pages = {809-810},
publisher = {IEEE Computer Society},
abstract = {Pose estimation is a common method for precise handtracking, which is important for natural interaction in virtual reality (VR). However, training those models requires large-scale datasets with accurate 3D annotations. Those are difficult to obtain due to the time-consuming data collection and the limited variety in captured scenarios. We present a work-in-progress Blender-based pipeline for generating synthetic multi-view hand datasets. Our system simulates Ultraleap Stereo IR 170-style images and extracts joint positions directly from a rigged hand model, eliminating the need for manual labeling or external tracking processes. The current pipeline version supports randomized static poses with per-frame annotations of joint positions, camera parameters, and rendered images. While extended hand variation, animation features, and different sensor-type simulations are still in progress, our pipeline already provides a flexible foundation for customizable dataset generation and reproducible hand-tracking model training.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Pose estimation is a common method for precise handtracking, which is important for natural interaction in virtual reality (VR). However, training those models requires large-scale datasets with accurate 3D annotations. Those are difficult to obtain due to the time-consuming data collection and the limited variety in captured scenarios. We present a work-in-progress Blender-based pipeline for generating synthetic multi-view hand datasets. Our system simulates Ultraleap Stereo IR 170-style images and extracts joint positions directly from a rigged hand model, eliminating the need for manual labeling or external tracking processes. The current pipeline version supports randomized static poses with per-frame annotations of joint positions, camera parameters, and rendered images. While extended hand variation, animation features, and different sensor-type simulations are still in progress, our pipeline already provides a flexible foundation for customizable dataset generation and reproducible hand-tracking model training. Alavi, Khashayar; Jansen, Anna; Steininger, Melissa; Mustafa, Sarah Al-Haj; Müllers, Johannes; Surges, Rainer; Helmstaedter, Christoph; von Wrede, Randi; Krüger, Björn
Graph Neural Networks for Analyzing Eye Fixation Patterns in Epilepsy Conference
International Congress on Mobile Health and Digital Technology in Epilepsy, 2025.
@conference{alavi2025a,
title = {Graph Neural Networks for Analyzing Eye Fixation Patterns in Epilepsy},
author = {Khashayar Alavi and Anna Jansen and Melissa Steininger and Sarah Al-Haj Mustafa and Johannes Müllers and Rainer Surges and Christoph Helmstaedter and Randi von Wrede and Björn Krüger},
year = {2025},
date = {2025-09-04},
urldate = {2025-09-04},
booktitle = {International Congress on Mobile Health and Digital Technology in Epilepsy},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Jansen, Anna; Steininger, Melissa; Mustafa, Sarah Al-Haj; Müllers, Johannes; Surges, Rainer; Helmstaedter, Christoph; von Wrede, Randi; Krüger, Björn
Search Behavior – Metrics for Analysis of Eye Tracking Data Conference
International Congress on Mobile Health and Digital Technology in Epilepsy, 2025.
@conference{jansen2025b,
title = {Search Behavior – Metrics for Analysis of Eye Tracking Data},
author = {Anna Jansen and Melissa Steininger and Sarah Al-Haj Mustafa and Johannes Müllers and Rainer Surges and Christoph Helmstaedter and Randi von Wrede and Björn Krüger},
year = {2025},
date = {2025-09-04},
urldate = {2025-09-04},
booktitle = {International Congress on Mobile Health and Digital Technology in Epilepsy},
journal = {International Congress on Mobile Health and Digital Technology in Epilepsy},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Pukropski, Jan; Weber, Christian; Müllers, Johannes; Grond, Martin; Surges, Rainer; Krüger, Björn
Implementation of a User-Friendly System in Epileptologic Teleconsultation Conference
International Congress on Mobile Health and Digital Technology in Epilepsy, 2025.
@conference{prukopski2025a,
title = {Implementation of a User-Friendly System in Epileptologic Teleconsultation},
author = {Jan Pukropski and Christian Weber and Johannes Müllers and Martin Grond and Rainer Surges and Björn Krüger},
year = {2025},
date = {2025-09-04},
urldate = {2025-09-04},
booktitle = {International Congress on Mobile Health and Digital Technology in Epilepsy},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Mustafa, Sarah Al-Haj; Jansen, Anna; Steininger, Melissa; Müllers, Johannes; Surges, Rainer; von Wrede, Randi; Krüger, Björn; Helmstaedter, Christoph
Eyes on Cognition: Exploring Oculomotor Correlates of Cognitive Function in Patients with Epilepsy Journal Article
In: Epilepsy & Behavior, vol. 173, iss. December 2025, no. 110562, 2025.
@article{alhaj2025,
title = {Eyes on Cognition: Exploring Oculomotor Correlates of Cognitive Function in Patients with Epilepsy},
author = {Sarah Al-Haj Mustafa and Anna Jansen and Melissa Steininger and Johannes Müllers and Rainer Surges and Randi von Wrede and Björn Krüger and Christoph Helmstaedter},
doi = {10.1016/j.yebeh.2025.110562},
year = {2025},
date = {2025-06-30},
urldate = {2025-06-30},
journal = {Epilepsy & Behavior},
volume = {173},
number = {110562},
issue = {December 2025},
abstract = {Objective
This study investigates the relationship between eye tracking parameters and cognitive performance during the Trail Making Test (TMT) in individuals with epilepsy and healthy controls. By analyzing ocular behaviors such as saccade velocity, fixation duration, and pupil diameter, we aim to determine how these metrics reflect executive functioning and attentional control.
Methods
A sample of 95 participants with epilepsy and 34 healthy controls completed the TMT while their eye movements were recorded. Partial correlations, controlling for age, sex, education, medication count, seizure status and epilepsy duration, examined associations between eye tracking measures and cognitive performance derived from EpiTrack and TMT performance.
Results
In the patient group, faster TMT-A performance was associated with shorter fix- ation durations (r = 0.31, p = 0.006). Lower minimum saccade velocity correlated with slower performance on both TMT-A (r = −0.35, p = 0.002) and TMT-B (r = −0.40, p<0.001), whereas higher peak saccade velocities were linked to worse performance (TMT-A: r = 0.45, p<0.001; TMT-B: r = 0.41, p<0.001). Pupil diameter findings indicated that slower TMT performance was associated with smaller minimum pupil sizes (r = −0.23 to r = −0.36), wich may indicate increased cognitive effort and attentional load. Higher EpiTrack scores also correlated with a smaller minimum pupil diameter − but only during the more demanding TMT-B − and with a more restricted saccade velocity range, reflecting greater motor control and attentional stability. No significant correlations emerged within the control group.
Conclusion
These findings highlight the potential of eye tracking as a non-invasive tool for assessing cognitive function in epilepsy. Efficient cognitive performance was characterized by stable and controlled eye movements, whereas impaired performance involved erratic saccade dynamics and prolonged fixations. Importantly, eye tracking parameters provide additional information beyond simple speed measurements, potentially enhancing the differential diagnostic capabilities of the TMT in epilepsy. The observed associations between oculomotor parameters and cognitive performance were not present in the control group, suggesting that these relationships may be specific to epilepsy. Future research should investigate whether both basic and advanced metrics of search strategies are sensitive to disease dynamics and treatment effects in epilepsy.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Objective
This study investigates the relationship between eye tracking parameters and cognitive performance during the Trail Making Test (TMT) in individuals with epilepsy and healthy controls. By analyzing ocular behaviors such as saccade velocity, fixation duration, and pupil diameter, we aim to determine how these metrics reflect executive functioning and attentional control.
Methods
A sample of 95 participants with epilepsy and 34 healthy controls completed the TMT while their eye movements were recorded. Partial correlations, controlling for age, sex, education, medication count, seizure status and epilepsy duration, examined associations between eye tracking measures and cognitive performance derived from EpiTrack and TMT performance.
Results
In the patient group, faster TMT-A performance was associated with shorter fix- ation durations (r = 0.31, p = 0.006). Lower minimum saccade velocity correlated with slower performance on both TMT-A (r = −0.35, p = 0.002) and TMT-B (r = −0.40, p<0.001), whereas higher peak saccade velocities were linked to worse performance (TMT-A: r = 0.45, p<0.001; TMT-B: r = 0.41, p<0.001). Pupil diameter findings indicated that slower TMT performance was associated with smaller minimum pupil sizes (r = −0.23 to r = −0.36), wich may indicate increased cognitive effort and attentional load. Higher EpiTrack scores also correlated with a smaller minimum pupil diameter − but only during the more demanding TMT-B − and with a more restricted saccade velocity range, reflecting greater motor control and attentional stability. No significant correlations emerged within the control group.
Conclusion
These findings highlight the potential of eye tracking as a non-invasive tool for assessing cognitive function in epilepsy. Efficient cognitive performance was characterized by stable and controlled eye movements, whereas impaired performance involved erratic saccade dynamics and prolonged fixations. Importantly, eye tracking parameters provide additional information beyond simple speed measurements, potentially enhancing the differential diagnostic capabilities of the TMT in epilepsy. The observed associations between oculomotor parameters and cognitive performance were not present in the control group, suggesting that these relationships may be specific to epilepsy. Future research should investigate whether both basic and advanced metrics of search strategies are sensitive to disease dynamics and treatment effects in epilepsy. Greß, Hannah; Demidova, Elena; Meier, Michael; Krüger, Björn
SecureNeuroAI: Advanced Security Framework for AI-Powered Multimodal Real-Time Detection of Medical Seizure Events Proceedings Article
In: Ohm, Marc (Ed.): Proceedings of the 15th graduate workshop of the special interest group Security - Intrusion Detection and Response (SIDAR) of the German Informatics Society (GI) (SPRING 2025), pp. 22-24, GI SIG SIDAR, Nuremberg, April, 2025, ISSN: 2190-846X.
@inproceedings{Greß2025,
title = {SecureNeuroAI: Advanced Security Framework for AI-Powered Multimodal Real-Time Detection of Medical Seizure Events},
author = {Hannah Greß and Elena Demidova and Michael Meier and Björn Krüger},
editor = {Marc Ohm},
url = {https://fg-sidar.gi.de/publikationen/sidar-reports},
issn = {2190-846X},
year = {2025},
date = {2025-05-12},
urldate = {2025-05-12},
booktitle = { Proceedings of the 15th graduate workshop of the special interest group Security - Intrusion Detection and Response (SIDAR) of the German Informatics Society (GI) (SPRING 2025)},
pages = {22-24},
publisher = {GI SIG SIDAR},
address = {Nuremberg, April},
abstract = {In today's interconnected world, medical devices are increasingly equipped with novel digital technologies and AI-powered methods to improve the users' quality of life.
Despite the increased possibilities and features these devices offer due to the technical progress, cyberattacks on medical devices will increase as well with possibly severe outcomes for the patients.
At the same time, AI-based technologies could help to detect and mitigate these attacks on medical systems and their data in real-time.
Therefore, our project "SecureNeuroAI" aims to detect epileptic seizures using multimodal sensor data and AI models while also considering possible cyberattacks on this system resulting in an IT-secure system.
Our results will serve as an example for future AI-supported medical devices and systems to enhance their security and to strengthen their trustworthiness towards their (future) users.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In today's interconnected world, medical devices are increasingly equipped with novel digital technologies and AI-powered methods to improve the users' quality of life.
Despite the increased possibilities and features these devices offer due to the technical progress, cyberattacks on medical devices will increase as well with possibly severe outcomes for the patients.
At the same time, AI-based technologies could help to detect and mitigate these attacks on medical systems and their data in real-time.
Therefore, our project "SecureNeuroAI" aims to detect epileptic seizures using multimodal sensor data and AI models while also considering possible cyberattacks on this system resulting in an IT-secure system.
Our results will serve as an example for future AI-supported medical devices and systems to enhance their security and to strengthen their trustworthiness towards their (future) users. Khan, Umar; Riaz, Qaiser; Hussain, Mehdi; Zeeshan, Muhammad; Krüger, Björn
Towards Effective Parkinson’s Monitoring: Movement Disorder Detection and Symptom Identification Using Wearable Inertial Sensors Journal Article
In: Algorithms, vol. 18, no. 4, 2025, ISSN: 1999-4893.
@article{2025-khan,
title = {Towards Effective Parkinson’s Monitoring: Movement Disorder Detection and Symptom Identification Using Wearable Inertial Sensors},
author = {Umar Khan and Qaiser Riaz and Mehdi Hussain and Muhammad Zeeshan and Björn Krüger},
url = {https://www.mdpi.com/1999-4893/18/4/203},
doi = {10.3390/a18040203},
issn = {1999-4893},
year = {2025},
date = {2025-04-04},
urldate = {2025-01-01},
journal = {Algorithms},
volume = {18},
number = {4},
abstract = {Parkinson’s disease lacks a cure, yet symptomatic relief can be achieved through various treatments. This study dives into the critical aspect of anomalous event detection in the activities of daily living of patients with Parkinson’s disease and the identification of associated movement disorders, such as tremors, dyskinesia, and bradykinesia. Utilizing the inertial data acquired from the most affected upper limb of the patients, this study aims to create an optimal pipeline for Parkinson’s patient monitoring. This study proposes a two-stage movement disorder detection and classification pipeline for binary classification (normal or anomalous event) and multi-label classification (tremors, dyskinesia, and bradykinesia), respectively. The proposed pipeline employs and evaluates manual feature crafting for classical machine learning algorithms, as well as an RNN-CNN-inspired deep learning model that does not require manual feature crafting. This study also explore three different window sizes for signal segmentation and two different auto-segment labeling approaches for precise and correct labeling of the continuous signal. The performance of the proposed model is validated on a publicly available inertial dataset. Comparisons with existing works reveal the novelty of our approach, covering multiple anomalies (tremors, dyskinesia, and bradykinesia) and achieving 93.03% recall for movement disorder detection (binary) and 91.54% recall for movement disorder classification (multi-label). We believe that the proposed approach will advance the field towards more effective and comprehensive solutions for Parkinson’s detection and symptom classification.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Parkinson’s disease lacks a cure, yet symptomatic relief can be achieved through various treatments. This study dives into the critical aspect of anomalous event detection in the activities of daily living of patients with Parkinson’s disease and the identification of associated movement disorders, such as tremors, dyskinesia, and bradykinesia. Utilizing the inertial data acquired from the most affected upper limb of the patients, this study aims to create an optimal pipeline for Parkinson’s patient monitoring. This study proposes a two-stage movement disorder detection and classification pipeline for binary classification (normal or anomalous event) and multi-label classification (tremors, dyskinesia, and bradykinesia), respectively. The proposed pipeline employs and evaluates manual feature crafting for classical machine learning algorithms, as well as an RNN-CNN-inspired deep learning model that does not require manual feature crafting. This study also explore three different window sizes for signal segmentation and two different auto-segment labeling approaches for precise and correct labeling of the continuous signal. The performance of the proposed model is validated on a publicly available inertial dataset. Comparisons with existing works reveal the novelty of our approach, covering multiple anomalies (tremors, dyskinesia, and bradykinesia) and achieving 93.03% recall for movement disorder detection (binary) and 91.54% recall for movement disorder classification (multi-label). We believe that the proposed approach will advance the field towards more effective and comprehensive solutions for Parkinson’s detection and symptom classification. Greß, Hannah; Alouardani, Saied; Hoffmann, Nico; Trebing, Pia; Becker, Albert J.; Surges, Rainer; Pitsch, Julika; Krüger, Björn
Digitale Transformation des Blutprobenmanagements Conference
Dreiländertagung Epilepsie 2025, 2025.
@conference{gress2025a,
title = {Digitale Transformation des Blutprobenmanagements},
author = {Hannah Greß and Saied Alouardani and Nico Hoffmann and Pia Trebing and Albert J. Becker and Rainer Surges and Julika Pitsch and Björn Krüger},
year = {2025},
date = {2025-03-26},
urldate = {2025-03-26},
booktitle = {Dreiländertagung Epilepsie 2025},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Haaga, Lisa; Jansen, Anna; Steininger, Melissa; Müllers, Johannes; Bausch, Marcel; Jordan, Arthur; Surges, Rainer; Krüger, Björn
EpiEye – Einfluss anfallssupressiver Medikamente auf Augenbewegungen und autonome Veränderungen bei Epilepsien Conference
Dreiländertagung Epilepsie 2025, 2025.
@conference{haaga2025a,
title = {EpiEye – Einfluss anfallssupressiver Medikamente auf Augenbewegungen und autonome Veränderungen bei Epilepsien},
author = {Lisa Haaga and Anna Jansen and Melissa Steininger and Johannes Müllers and Marcel Bausch and Arthur Jordan and Rainer Surges and Björn Krüger},
year = {2025},
date = {2025-03-26},
booktitle = {Dreiländertagung Epilepsie 2025},
keywords = {},
pubstate = {published},
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}
Müllers, Johannes; Greß, Hannah; Weber, Christian; Nadeem, Mubaris; Hütwohl, Daniela; Pukropski, Jan; Grond, Martin; Surges, Rainer; Krüger, Björn
Aufbau eines epileptologischen Telekonsils zwischen dem Klinikum Siegen und dem Universitätsklinikum Bonn Conference
Dreiländertagung Epilepsie 2025, 2025.
@conference{muellers2025a,
title = {Aufbau eines epileptologischen Telekonsils zwischen dem Klinikum Siegen und dem Universitätsklinikum Bonn},
author = {Johannes Müllers and Hannah Greß and Christian Weber and Mubaris Nadeem and Daniela Hütwohl and Jan Pukropski and Martin Grond and Rainer Surges and Björn Krüger},
year = {2025},
date = {2025-03-26},
urldate = {2025-03-26},
booktitle = {Dreiländertagung Epilepsie 2025},
keywords = {},
pubstate = {published},
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}
Mustafa, Sarah Al-Haj; Jansen, Anna; Surges, Rainer; Helmstaedter, Christoph; von Wrede, Randi; Krüger, Björn
ANNE – Augen-Tracking zur Erkennung von Nebenwirkungen bei Epilepsie Conference
Dreiländertagung Epilepsie 2025, 2025.
@conference{nokey,
title = {ANNE – Augen-Tracking zur Erkennung von Nebenwirkungen bei Epilepsie},
author = {Sarah Al-Haj Mustafa and Anna Jansen and Rainer Surges and Christoph Helmstaedter and Randi von Wrede and Björn Krüger},
year = {2025},
date = {2025-03-26},
urldate = {2025-03-26},
booktitle = {Dreiländertagung Epilepsie 2025},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
2026
Gfesser, Torsten; Witte, Thomas; Krüger, Björn
From Groups to Individuals: Generalization Challenges of HRV Based Classifiers Proceedings Article Forthcoming
In: HCI International 2026, Springer, Forthcoming.
@inproceedings{gfesser2026a,
title = {From Groups to Individuals: Generalization Challenges of HRV Based Classifiers},
author = {Torsten Gfesser and Thomas Witte and Björn Krüger},
year = {2026},
date = {2026-07-31},
urldate = {2026-07-31},
booktitle = {HCI International 2026},
publisher = {Springer},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
Gfesser, Torsten; Witte, Thomas; Krüger, Björn
On the Efficacy and Usability of Adaptive Instructional Systems Proceedings Article Forthcoming
In: HCI International 2026, Springer, Forthcoming.
@inproceedings{nokey,
title = {On the Efficacy and Usability of Adaptive Instructional Systems},
author = {Torsten Gfesser and Thomas Witte and Björn Krüger},
year = {2026},
date = {2026-07-31},
urldate = {2026-07-31},
booktitle = {HCI International 2026},
publisher = {Springer},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
Kretschmer-Trendowicz, Anett; Moser, Florian; Gürster, Lena; Pippirs, Corinna; Maas, Pia; Zeiler, Anne; Steininger, Melissa; Walk, Simon; von Bock, Christian; Krüger, Björn; Spittler, Thomas
Virtual Interaction to Promote Mental Health in Children with Social Anxiety Disorders (VISAKI) Conference Forthcoming
European Congress of Psychiatry 2026, Forthcoming.
@conference{kretschmer2026a,
title = {Virtual Interaction to Promote Mental Health in Children with Social Anxiety Disorders (VISAKI)},
author = {Anett Kretschmer-Trendowicz and Florian Moser and Lena Gürster and Corinna Pippirs and Pia Maas and Anne Zeiler and Melissa Steininger and Simon Walk and Christian von Bock and Björn Krüger and Thomas Spittler},
year = {2026},
date = {2026-04-01},
booktitle = {European Congress of Psychiatry 2026},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
Steininger, Melissa; Jansen, Anna; Mustafa, Sarah Al-Haj; Bouzan, Nataly; Surges, Rainer; Helmstaedter, Christoph; von Wrede, Randi; Krüger, Björn
Linking Higher-level Eye Tracking Metrics to High-Impact Antiseizure Medication in Epilepsy Patients Conference Forthcoming
4th International Conference on Artificial Intelligence in Epilepsy and Neurological Disorders, Forthcoming.
@conference{steininger2026a,
title = {Linking Higher-level Eye Tracking Metrics to High-Impact Antiseizure Medication in Epilepsy Patients},
author = {Melissa Steininger and Anna Jansen and Sarah Al-Haj Mustafa and Nataly Bouzan and Rainer Surges and Christoph Helmstaedter and Randi von Wrede and Björn Krüger},
year = {2026},
date = {2026-03-31},
booktitle = {4th International Conference on Artificial Intelligence in Epilepsy and Neurological Disorders},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
Jansen, Anna; Waldow, Kristoffer; Pötter, Sebastian; Civelek, Turhan; Steininger, Melissa; Perret, Jerome; Wellmann, Markus; Stein, Steffen-Sascha; Lähner, David; Welle, Kristian; Fuhrmann, Arnulph; Krüger, Björn
VIRTOSHA - A VR Training Simulation for Osteosynthesis Procedures with Force Feedback and Tissue Simulation Proceedings Article Forthcoming
In: IEEE VR 2026 Workshop: XR-MED, Forthcoming.
@inproceedings{jansen2026b,
title = {VIRTOSHA - A VR Training Simulation for Osteosynthesis Procedures with Force Feedback and Tissue Simulation},
author = {Anna Jansen and Kristoffer Waldow and Sebastian Pötter and Turhan Civelek and Melissa Steininger and Jerome Perret and Markus Wellmann and Steffen-Sascha Stein and David Lähner and Kristian Welle and Arnulph Fuhrmann and Björn Krüger},
year = {2026},
date = {2026-03-31},
urldate = {2026-03-31},
booktitle = {IEEE VR 2026 Workshop: XR-MED},
abstract = {Osteosynthesis training requires development of force-sensitive manual skills and an understanding of workflows, which are difficult to acquire through theoretical instruction or cadaver-based training. While Virtual Reality (VR) offers new opportunities for surgical training, existing systems often focus on isolated subtasks, lacking integrated support for realistic interaction, procedural logic, and adaptability. This paper presents a work-in-progress VR training system designed for workflow-oriented osteosynthesis training. The system combines force feedback, physics-based tissue simulation and robust hand tracking in a modular architecture. Additionally, an expert-driven authoring workflow enables medical professionals to define and adapt training scenarios without programming.
Using a reference scenario for fibular fracture osteosynthesis, we describe the system design, core components, and current implementation status. We further discuss technical trade-offs, limitations, and directions for future validation. Our system establishes a foundation for force-sensitive, workflow-oriented VR training and serves as a basis for future studies in surgical education.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
Osteosynthesis training requires development of force-sensitive manual skills and an understanding of workflows, which are difficult to acquire through theoretical instruction or cadaver-based training. While Virtual Reality (VR) offers new opportunities for surgical training, existing systems often focus on isolated subtasks, lacking integrated support for realistic interaction, procedural logic, and adaptability. This paper presents a work-in-progress VR training system designed for workflow-oriented osteosynthesis training. The system combines force feedback, physics-based tissue simulation and robust hand tracking in a modular architecture. Additionally, an expert-driven authoring workflow enables medical professionals to define and adapt training scenarios without programming.
Using a reference scenario for fibular fracture osteosynthesis, we describe the system design, core components, and current implementation status. We further discuss technical trade-offs, limitations, and directions for future validation. Our system establishes a foundation for force-sensitive, workflow-oriented VR training and serves as a basis for future studies in surgical education.
Using a reference scenario for fibular fracture osteosynthesis, we describe the system design, core components, and current implementation status. We further discuss technical trade-offs, limitations, and directions for future validation. Our system establishes a foundation for force-sensitive, workflow-oriented VR training and serves as a basis for future studies in surgical education.
Steininger, Melissa; Jansen, Anna; Müllers, Johannes; von Wrede, Randi; Krüger, Björn
Toward Interpretable Cognitive Screening in Epilepsy: Eye Tracking in a VR Trail Making Test Proceedings Article Forthcoming
In: IEEE VR 2026 Workshop: GEMINI, Forthcoming.
@inproceedings{steininger2026b,
title = {Toward Interpretable Cognitive Screening in Epilepsy: Eye Tracking in a VR Trail Making Test},
author = {Melissa Steininger and Anna Jansen and Johannes Müllers and Randi von Wrede and Björn Krüger},
year = {2026},
date = {2026-03-31},
urldate = {2026-03-31},
booktitle = {IEEE VR 2026 Workshop: GEMINI},
abstract = {Cognitive screening is a routine component of epilepsy care. Established pen-and-paper instruments such as the Trail Making Test (TMT) primarily yield summary outcomes (e.g., completion time) that provide limited insight into visual search and executive-control processes affected by epilepsy-related brain network dysfunction. We present an eye-tracked Virtual Reality TMT (VR-TMT) as a controlled research instrument that enables process-level interpretable measurements. The system synchronizes continuous eye-movement streams with timestamped task events (task start/stop and node selections) and logs gaze-to-Area-of-Interest (AOI) intersections. To reduce VR-specific confounds that can compromise cognitive interpretation, we specify concrete design guidelines for 3D stimulus geometry and the VR+eye-tracking setup (e.g., viewing distance, field-of-view placement, target size).
In a feasibility pilot (n=8) usability ratings were favorable and cybersickness was low. Building on this foundation, we outline an analysis framework that derives contextualized gaze features and evaluates their added value in explaining established cognitive screening outcomes in epilepsy cohorts.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
Cognitive screening is a routine component of epilepsy care. Established pen-and-paper instruments such as the Trail Making Test (TMT) primarily yield summary outcomes (e.g., completion time) that provide limited insight into visual search and executive-control processes affected by epilepsy-related brain network dysfunction. We present an eye-tracked Virtual Reality TMT (VR-TMT) as a controlled research instrument that enables process-level interpretable measurements. The system synchronizes continuous eye-movement streams with timestamped task events (task start/stop and node selections) and logs gaze-to-Area-of-Interest (AOI) intersections. To reduce VR-specific confounds that can compromise cognitive interpretation, we specify concrete design guidelines for 3D stimulus geometry and the VR+eye-tracking setup (e.g., viewing distance, field-of-view placement, target size).
In a feasibility pilot (n=8) usability ratings were favorable and cybersickness was low. Building on this foundation, we outline an analysis framework that derives contextualized gaze features and evaluates their added value in explaining established cognitive screening outcomes in epilepsy cohorts.
In a feasibility pilot (n=8) usability ratings were favorable and cybersickness was low. Building on this foundation, we outline an analysis framework that derives contextualized gaze features and evaluates their added value in explaining established cognitive screening outcomes in epilepsy cohorts.
Jansen, Anna; Steininger, Melissa; Mustafa, Sarah Al-Haj; Bouzan, Nataly; Surges, Rainer; Helmstaedter, Christoph; von Wrede, Randi; Krüger, Björn
Higher-Level Eye Tracking Metrics Reveal Search Behaviour Differences in Persons with Epilepsy vs. Healthy Controls Conference Forthcoming
4th International Conference on Artificial Intelligence in Epilepsy and Neurological Disorders, Forthcoming.
@conference{jansen2026a,
title = {Higher-Level Eye Tracking Metrics Reveal Search Behaviour Differences in Persons with Epilepsy vs. Healthy Controls},
author = {Anna Jansen and Melissa Steininger and Sarah Al-Haj Mustafa and Nataly Bouzan and Rainer Surges and Christoph Helmstaedter and Randi von Wrede and Björn Krüger},
year = {2026},
date = {2026-03-30},
booktitle = {4th International Conference on Artificial Intelligence in Epilepsy and Neurological Disorders},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
Müllers, Johannes; Siddiquie, Usama; Lemken, Johannes; Staehle, Ricarda; Schulte-Rüther, Martin; Krüger, Björn
MARVEL: A Human-in-the-Loop Web Platform for Multimodal Annotation and Classification of Social Behavior Conference Forthcoming
17th Autism Spectrum Scientific Conference, Forthcoming.
@conference{Muellers2026,
title = {MARVEL: A Human-in-the-Loop Web Platform for Multimodal Annotation and Classification of Social Behavior},
author = {Johannes Müllers and Usama Siddiquie and Johannes Lemken and Ricarda Staehle and Martin Schulte-Rüther and Björn Krüger},
year = {2026},
date = {2026-03-14},
urldate = {2026-03-14},
booktitle = {17th Autism Spectrum Scientific Conference},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
Staehle, Ricarda; Siddiquie, Usama; Müllers, Johannes; Krüger, Björn; Poustka, Luise; Schulte-Rüther, Martin
Clinical Annotation of Socio-Emotional Signals in Autism: Facilitating Diagnostic Review, Consensus Building, and Machine Learning Applications Conference Forthcoming
17th Autism Spectrum Scientific Conference, Forthcoming.
@conference{nokey,
title = {Clinical Annotation of Socio-Emotional Signals in Autism: Facilitating Diagnostic Review, Consensus Building, and Machine Learning Applications},
author = {Ricarda Staehle and Usama Siddiquie and Johannes Müllers and Björn Krüger and Luise Poustka and Martin Schulte-Rüther},
year = {2026},
date = {2026-03-14},
booktitle = {17th Autism Spectrum Scientific Conference},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
Goharinejad, Saeideh; Goharinezhad, Salime; Moulaei, Khadijeh; Krüger, Björn; Spittler, Thomas
In: INQUIRY: The Journal of Health Care Organization, Provision, and Financing, vol. 63, pp. 00469580251413101, 2026.
@article{Goharinejad-2025,
title = {Assessing the Impact of Virtual Reality, Augmented Reality, and Video Games on Improving Post-Traumatic Stress Disorder Symptoms: A Systematic Review and Meta-Analysis},
author = {Saeideh Goharinejad and Salime Goharinezhad and Khadijeh Moulaei and Björn Krüger and Thomas Spittler},
url = {https://doi.org/10.1177/00469580251413101},
doi = {10.1177/00469580251413101},
year = {2026},
date = {2026-01-28},
urldate = {2025-12-01},
journal = {INQUIRY: The Journal of Health Care Organization, Provision, and Financing},
volume = {63},
pages = {00469580251413101},
abstract = {Post-traumatic stress disorder (PTSD) is often debilitating, with current treatments limited by low adherence, high costs, and accessibility issues. Innovative technologies such as virtual reality (VR), augmented reality (AR), and therapeutic video games provide immersive environments that may improve treatment outcomes. This systematic review and meta-analysis evaluated the efficacy of these approaches and explored their potential advantages over traditional methods. A comprehensive search of PubMed, PsycINFO, CINAHL, Web of Science, and Cochrane identified relevant studies. Two reviewers independently screened articles, extracted data, and assessed quality using the Mixed Methods Appraisal Tool (MMAT). A random-effects model was used to calculate pooled effect sizes (Hedges’ g), and heterogeneity was evaluated with the Q test and I2 statistic. Publication bias was examined with funnel plots, Egger’s, and Begg’s tests. Analyses were performed in Stata version 17.0. From 480 records, 21 studies were included in the review and 12 in the meta-analysis. VR-based treatments yielded a pooled effect size of –0.35 (95% CI [–0.57, –0.13]), indicating a small-to-moderate reduction in PTSD symptoms. The effect was statistically significant (z = –3.13, P < .01), with moderate heterogeneity (I2 = 46.28%, P = .03). Funnel plots and statistical tests suggested minimal publication bias. Meta-regression showed no moderating effect of gender. Subgroup analyses indicated significant benefits in male-only samples, participants aged 20 to 30 and over 40, and studies with follow-up periods ≤7 months. Larger effects were observed in studies with 15 to 30 participants. VR, AR, and video game interventions significantly reduce PTSD symptoms and may enhance accessibility and engagement compared to traditional treatments. These findings support the integration of immersive technologies into therapeutic practice to improve outcomes for individuals with PTSD. }
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Post-traumatic stress disorder (PTSD) is often debilitating, with current treatments limited by low adherence, high costs, and accessibility issues. Innovative technologies such as virtual reality (VR), augmented reality (AR), and therapeutic video games provide immersive environments that may improve treatment outcomes. This systematic review and meta-analysis evaluated the efficacy of these approaches and explored their potential advantages over traditional methods. A comprehensive search of PubMed, PsycINFO, CINAHL, Web of Science, and Cochrane identified relevant studies. Two reviewers independently screened articles, extracted data, and assessed quality using the Mixed Methods Appraisal Tool (MMAT). A random-effects model was used to calculate pooled effect sizes (Hedges’ g), and heterogeneity was evaluated with the Q test and I2 statistic. Publication bias was examined with funnel plots, Egger’s, and Begg’s tests. Analyses were performed in Stata version 17.0. From 480 records, 21 studies were included in the review and 12 in the meta-analysis. VR-based treatments yielded a pooled effect size of –0.35 (95% CI [–0.57, –0.13]), indicating a small-to-moderate reduction in PTSD symptoms. The effect was statistically significant (z = –3.13, P < .01), with moderate heterogeneity (I2 = 46.28%, P = .03). Funnel plots and statistical tests suggested minimal publication bias. Meta-regression showed no moderating effect of gender. Subgroup analyses indicated significant benefits in male-only samples, participants aged 20 to 30 and over 40, and studies with follow-up periods ≤7 months. Larger effects were observed in studies with 15 to 30 participants. VR, AR, and video game interventions significantly reduce PTSD symptoms and may enhance accessibility and engagement compared to traditional treatments. These findings support the integration of immersive technologies into therapeutic practice to improve outcomes for individuals with PTSD. }
2025
Bhatti, Faraz Ahmad; Riaz, Qaiser; Krüger, Björn
Beyond Falls: A Hybrid CNN–LSTM–Attention Framework for Pre-, Transition-, and Post-Fall Detection with Wearable Inertial Sensors Journal Article
In: IEEE Access, 2025.
@article{Bhatti2025,
title = {Beyond Falls: A Hybrid CNN–LSTM–Attention Framework for Pre-, Transition-, and Post-Fall Detection with Wearable Inertial Sensors},
author = {Faraz Ahmad Bhatti and Qaiser Riaz and Björn Krüger},
doi = {10.1109/ACCESS.2025.3641198},
year = {2025},
date = {2025-12-05},
urldate = {2025-12-02},
journal = {IEEE Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Barzegar, Mohammad Mehdi; Daryakenari, Nazila Ahmadi; Khodatars, Marjane
Explainable Epileptic Seizure Detection from Electroencephalography Signals via CNN–Bi-LSTM Attention Hybrid Model Journal Article
In: Journal of Research and Health, vol. 15, no. 6, 2025.
@article{Barzegar2025,
title = {Explainable Epileptic Seizure Detection from Electroencephalography Signals via CNN–Bi-LSTM Attention Hybrid Model},
author = {Mohammad Mehdi Barzegar and Nazila Ahmadi Daryakenari and Marjane Khodatars},
url = {http://jrh.gmu.ac.ir/article-1-2987-en.html},
doi = {10.32598/JRH.15.SP.2892.1},
year = {2025},
date = {2025-12-01},
urldate = {2025-12-01},
journal = {Journal of Research and Health},
volume = {15},
number = {6},
abstract = {Background: Epilepsy is a chronic neurological disorder marked by recurrent daily seizures that threaten patient safety. Electroencephalography (EEG) is a crucial neuroimaging tool for epilepsy diagnosis, but manual interpretation of EEG signals is challenging for clinicians. To assist specialists, automated systems, such as computer-aided diagnosis systems (CADS) based on deep learning (DL) are essential. Methods: The proposed CADS system was validated using the Turkish epilepsy dataset. In preprocessing, EEG signals were filtered, down-sampled, re-referenced using common average reference (CAR), and segmented into multiple temporal windows. A new feature extraction framework combining one-dimensional convolutional neural networks (1D-CNN), bidirectional long short-term memory (Bi-LSTM), and an attention mechanism was developed. All experiments were performed using 5-fold cross-validation. Post-hoc explainability was evaluated using explainable artificial intelligence (XAI) techniques, including t-distributed stochastic neighbor embedding (t-SNE) and shapley additive explanations (SHAP). Results: The proposed CADS achieved a seizure diagnosis accuracy of 99.49%, demonstrating high robustness across the validation folds, with minimal variance between folds (±0.12%). Feature space visualization confirmed clear class separation, and SHAP analysis provided clinically meaningful explanations for model decisions. Conclusion: The proposed DL architecture shows strong potential for reliable and interpretable automatic epileptic seizure detection from EEG. This CADS can significantly reduce the diagnostic burden on clinicians and support real-time decision-making in clinical environments.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: Epilepsy is a chronic neurological disorder marked by recurrent daily seizures that threaten patient safety. Electroencephalography (EEG) is a crucial neuroimaging tool for epilepsy diagnosis, but manual interpretation of EEG signals is challenging for clinicians. To assist specialists, automated systems, such as computer-aided diagnosis systems (CADS) based on deep learning (DL) are essential. Methods: The proposed CADS system was validated using the Turkish epilepsy dataset. In preprocessing, EEG signals were filtered, down-sampled, re-referenced using common average reference (CAR), and segmented into multiple temporal windows. A new feature extraction framework combining one-dimensional convolutional neural networks (1D-CNN), bidirectional long short-term memory (Bi-LSTM), and an attention mechanism was developed. All experiments were performed using 5-fold cross-validation. Post-hoc explainability was evaluated using explainable artificial intelligence (XAI) techniques, including t-distributed stochastic neighbor embedding (t-SNE) and shapley additive explanations (SHAP). Results: The proposed CADS achieved a seizure diagnosis accuracy of 99.49%, demonstrating high robustness across the validation folds, with minimal variance between folds (±0.12%). Feature space visualization confirmed clear class separation, and SHAP analysis provided clinically meaningful explanations for model decisions. Conclusion: The proposed DL architecture shows strong potential for reliable and interpretable automatic epileptic seizure detection from EEG. This CADS can significantly reduce the diagnostic burden on clinicians and support real-time decision-making in clinical environments.
Moontaha, Sidratul; Cavalier, Constanze; Esser, Birgitta; Jordan, Arthur; Goebel, Ines; Anders, Christoph; Mimi, Afsana; Krüger, Björn; Surges, Rainer; Arnrich, Bert
EPIStress: A multimodal dataset of Physiological signals to measure cognitive stress in epilepsy patients Journal Article
In: Scientific Data, vol. 12, iss. 1, no. 1867, 2025, ISBN: 2052-4463.
@article{Moontaha2025,
title = {EPIStress: A multimodal dataset of Physiological signals to measure cognitive stress in epilepsy patients},
author = {Sidratul Moontaha and Constanze Cavalier and Birgitta Esser and Arthur Jordan and Ines Goebel and Christoph Anders and Afsana Mimi and Björn Krüger and Rainer Surges and Bert Arnrich},
url = {https://doi.org/10.1038/s41597-025-06328-3},
doi = {10.1038/s41597-025-06328-3},
isbn = {2052-4463},
year = {2025},
date = {2025-11-28},
urldate = {2025-12-01},
journal = {Scientific Data},
volume = {12},
number = {1867},
issue = {1},
abstract = {Epilepsy patients commonly report stress as a frequent seizure trigger; however, the objective seizure-stress relationship is unclear due to self-report biases and difficulty in objective quantification of stress. This work presents a dataset from twenty epilepsy patients undergoing cognitive stress elicitation protocols, participating in laboratory experiments with computer-based tasks at predefined difficulty levels, and in situational experiments by independently choosing tasks with at least two difficulty levels. Physiological signals from wearable electroencephalography, photoplethysmography, acceleration, electrodermal activity, and temperature sensors were recorded. The task-related perceived cognitive stress was collected using two 5-point Likert scales of self-reported mental workload and stress, contrasted by a pairwise NASA-TLX questionnaire. Additionally, the dataset includes a patient-reported list of seizure-provoking and -inhibiting factors. Results illustrated individual and heterogeneous responses to cognitive tasks, with some modalities yielding statistically significant features, while others demonstrated expected directional trends. The findings support the validity and suitability of the proposed dataset for cognitive stress detection and the potential to map seizure-related factors to cognitive stress events.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Epilepsy patients commonly report stress as a frequent seizure trigger; however, the objective seizure-stress relationship is unclear due to self-report biases and difficulty in objective quantification of stress. This work presents a dataset from twenty epilepsy patients undergoing cognitive stress elicitation protocols, participating in laboratory experiments with computer-based tasks at predefined difficulty levels, and in situational experiments by independently choosing tasks with at least two difficulty levels. Physiological signals from wearable electroencephalography, photoplethysmography, acceleration, electrodermal activity, and temperature sensors were recorded. The task-related perceived cognitive stress was collected using two 5-point Likert scales of self-reported mental workload and stress, contrasted by a pairwise NASA-TLX questionnaire. Additionally, the dataset includes a patient-reported list of seizure-provoking and -inhibiting factors. Results illustrated individual and heterogeneous responses to cognitive tasks, with some modalities yielding statistically significant features, while others demonstrated expected directional trends. The findings support the validity and suitability of the proposed dataset for cognitive stress detection and the potential to map seizure-related factors to cognitive stress events.
Steininger, Melissa; Marquardt, Alexander; Perusquía-Hernández, Monica; Lehnort, Marvin; Otsubo, Hiromu; Dollack, Felix; Kruijff, Ernst; Krüger, Björn; Kiyokawa, Kiyoshi; Riecke, Bernhard E.
The Awe-some Spectrum: Self-Reported Awe Varies by Eliciting Scenery and Presence in Virtual Reality, and the User's Nationality Proceedings Article
In: 2025 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 1267-1277, 2025.
@inproceedings{steininger2025c,
title = {The Awe-some Spectrum: Self-Reported Awe Varies by Eliciting Scenery and Presence in Virtual Reality, and the User's Nationality},
author = {Melissa Steininger and Alexander Marquardt and Monica Perusquía-Hernández and Marvin Lehnort and Hiromu Otsubo and Felix Dollack and Ernst Kruijff and Björn Krüger and Kiyoshi Kiyokawa and Bernhard E. Riecke
},
doi = {10.1109/ISMAR67309.2025.00132},
year = {2025},
date = {2025-11-11},
urldate = {2025-10-01},
booktitle = {2025 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)},
pages = {1267-1277},
abstract = {Awe is a multifaceted emotion often associated with the perception of vastness, that challenges existing mental frameworks. Despite its growing relevance in affective computing and psychological research, awe remains difficult to elicit and measure.
This raises the research questions of how awe can be effectively elicited, which factors are associated with the experience of awe, and whether it can reliably be measured using biosensors.
For this study, we designed ten immersive Virtual Reality (VR) scenes with dynamic transitions from narrow to vast environments. These scenes were used to explore how awe relates to environmental features (abstract, human-made, nature), personality traits, and country of origin. We collected skin conductance, respiration data, and self-reported awe and presence from participants from Germany, Japan, and Jordan.
Our results indicate that self-reported awe varies significantly across countries and scene types. In particular, a scene depicting outer space elicited the strongest awe. Scenes that elicited high self-reported awe also induced a stronger sense of presence. However, we found no evidence that awe ratings are correlated with physiological responses.
These findings challenge the assumption that awe is reliably reflected in autonomic arousal and underscore the importance of cultural and perceptual context.
Our study offers new insights into how immersive VR can be designed to elicit awe, and suggests that subjective reports—rather than physiological signals—remain the most consistent indicators of emotional impact.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Awe is a multifaceted emotion often associated with the perception of vastness, that challenges existing mental frameworks. Despite its growing relevance in affective computing and psychological research, awe remains difficult to elicit and measure.
This raises the research questions of how awe can be effectively elicited, which factors are associated with the experience of awe, and whether it can reliably be measured using biosensors.
For this study, we designed ten immersive Virtual Reality (VR) scenes with dynamic transitions from narrow to vast environments. These scenes were used to explore how awe relates to environmental features (abstract, human-made, nature), personality traits, and country of origin. We collected skin conductance, respiration data, and self-reported awe and presence from participants from Germany, Japan, and Jordan.
Our results indicate that self-reported awe varies significantly across countries and scene types. In particular, a scene depicting outer space elicited the strongest awe. Scenes that elicited high self-reported awe also induced a stronger sense of presence. However, we found no evidence that awe ratings are correlated with physiological responses.
These findings challenge the assumption that awe is reliably reflected in autonomic arousal and underscore the importance of cultural and perceptual context.
Our study offers new insights into how immersive VR can be designed to elicit awe, and suggests that subjective reports—rather than physiological signals—remain the most consistent indicators of emotional impact.
This raises the research questions of how awe can be effectively elicited, which factors are associated with the experience of awe, and whether it can reliably be measured using biosensors.
For this study, we designed ten immersive Virtual Reality (VR) scenes with dynamic transitions from narrow to vast environments. These scenes were used to explore how awe relates to environmental features (abstract, human-made, nature), personality traits, and country of origin. We collected skin conductance, respiration data, and self-reported awe and presence from participants from Germany, Japan, and Jordan.
Our results indicate that self-reported awe varies significantly across countries and scene types. In particular, a scene depicting outer space elicited the strongest awe. Scenes that elicited high self-reported awe also induced a stronger sense of presence. However, we found no evidence that awe ratings are correlated with physiological responses.
These findings challenge the assumption that awe is reliably reflected in autonomic arousal and underscore the importance of cultural and perceptual context.
Our study offers new insights into how immersive VR can be designed to elicit awe, and suggests that subjective reports—rather than physiological signals—remain the most consistent indicators of emotional impact.
Jansen, Anna; Morev, Nikita; Steininger, Melissa; Müllers, Johannes; Krüger, Björn
Synthetic Hand Dataset Generation: Multi-View Rendering and Annotation with Blender Proceedings Article
In: 2025 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), pp. 809-810, IEEE Computer Society, 2025.
@inproceedings{jansen2025c,
title = {Synthetic Hand Dataset Generation: Multi-View Rendering and Annotation with Blender},
author = {Anna Jansen and Nikita Morev and Melissa Steininger and Johannes Müllers and Björn Krüger},
url = {https://www.computer.org/csdl/proceedings-article/ismar-adjunct/2025/934700a809/2bKcNnpvzTG},
doi = {10.1109/ISMAR-Adjunct68609.2025.00201},
year = {2025},
date = {2025-10-06},
urldate = {2025-10-06},
booktitle = {2025 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)},
pages = {809-810},
publisher = {IEEE Computer Society},
abstract = {Pose estimation is a common method for precise handtracking, which is important for natural interaction in virtual reality (VR). However, training those models requires large-scale datasets with accurate 3D annotations. Those are difficult to obtain due to the time-consuming data collection and the limited variety in captured scenarios. We present a work-in-progress Blender-based pipeline for generating synthetic multi-view hand datasets. Our system simulates Ultraleap Stereo IR 170-style images and extracts joint positions directly from a rigged hand model, eliminating the need for manual labeling or external tracking processes. The current pipeline version supports randomized static poses with per-frame annotations of joint positions, camera parameters, and rendered images. While extended hand variation, animation features, and different sensor-type simulations are still in progress, our pipeline already provides a flexible foundation for customizable dataset generation and reproducible hand-tracking model training.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Pose estimation is a common method for precise handtracking, which is important for natural interaction in virtual reality (VR). However, training those models requires large-scale datasets with accurate 3D annotations. Those are difficult to obtain due to the time-consuming data collection and the limited variety in captured scenarios. We present a work-in-progress Blender-based pipeline for generating synthetic multi-view hand datasets. Our system simulates Ultraleap Stereo IR 170-style images and extracts joint positions directly from a rigged hand model, eliminating the need for manual labeling or external tracking processes. The current pipeline version supports randomized static poses with per-frame annotations of joint positions, camera parameters, and rendered images. While extended hand variation, animation features, and different sensor-type simulations are still in progress, our pipeline already provides a flexible foundation for customizable dataset generation and reproducible hand-tracking model training.
Alavi, Khashayar; Jansen, Anna; Steininger, Melissa; Mustafa, Sarah Al-Haj; Müllers, Johannes; Surges, Rainer; Helmstaedter, Christoph; von Wrede, Randi; Krüger, Björn
Graph Neural Networks for Analyzing Eye Fixation Patterns in Epilepsy Conference
International Congress on Mobile Health and Digital Technology in Epilepsy, 2025.
@conference{alavi2025a,
title = {Graph Neural Networks for Analyzing Eye Fixation Patterns in Epilepsy},
author = {Khashayar Alavi and Anna Jansen and Melissa Steininger and Sarah Al-Haj Mustafa and Johannes Müllers and Rainer Surges and Christoph Helmstaedter and Randi von Wrede and Björn Krüger},
year = {2025},
date = {2025-09-04},
urldate = {2025-09-04},
booktitle = {International Congress on Mobile Health and Digital Technology in Epilepsy},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Jansen, Anna; Steininger, Melissa; Mustafa, Sarah Al-Haj; Müllers, Johannes; Surges, Rainer; Helmstaedter, Christoph; von Wrede, Randi; Krüger, Björn
Search Behavior – Metrics for Analysis of Eye Tracking Data Conference
International Congress on Mobile Health and Digital Technology in Epilepsy, 2025.
@conference{jansen2025b,
title = {Search Behavior – Metrics for Analysis of Eye Tracking Data},
author = {Anna Jansen and Melissa Steininger and Sarah Al-Haj Mustafa and Johannes Müllers and Rainer Surges and Christoph Helmstaedter and Randi von Wrede and Björn Krüger},
year = {2025},
date = {2025-09-04},
urldate = {2025-09-04},
booktitle = {International Congress on Mobile Health and Digital Technology in Epilepsy},
journal = {International Congress on Mobile Health and Digital Technology in Epilepsy},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Pukropski, Jan; Weber, Christian; Müllers, Johannes; Grond, Martin; Surges, Rainer; Krüger, Björn
Implementation of a User-Friendly System in Epileptologic Teleconsultation Conference
International Congress on Mobile Health and Digital Technology in Epilepsy, 2025.
@conference{prukopski2025a,
title = {Implementation of a User-Friendly System in Epileptologic Teleconsultation},
author = {Jan Pukropski and Christian Weber and Johannes Müllers and Martin Grond and Rainer Surges and Björn Krüger},
year = {2025},
date = {2025-09-04},
urldate = {2025-09-04},
booktitle = {International Congress on Mobile Health and Digital Technology in Epilepsy},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Mustafa, Sarah Al-Haj; Jansen, Anna; Steininger, Melissa; Müllers, Johannes; Surges, Rainer; von Wrede, Randi; Krüger, Björn; Helmstaedter, Christoph
Eyes on Cognition: Exploring Oculomotor Correlates of Cognitive Function in Patients with Epilepsy Journal Article
In: Epilepsy & Behavior, vol. 173, iss. December 2025, no. 110562, 2025.
@article{alhaj2025,
title = {Eyes on Cognition: Exploring Oculomotor Correlates of Cognitive Function in Patients with Epilepsy},
author = {Sarah Al-Haj Mustafa and Anna Jansen and Melissa Steininger and Johannes Müllers and Rainer Surges and Randi von Wrede and Björn Krüger and Christoph Helmstaedter},
doi = {10.1016/j.yebeh.2025.110562},
year = {2025},
date = {2025-06-30},
urldate = {2025-06-30},
journal = {Epilepsy & Behavior},
volume = {173},
number = {110562},
issue = {December 2025},
abstract = {Objective
This study investigates the relationship between eye tracking parameters and cognitive performance during the Trail Making Test (TMT) in individuals with epilepsy and healthy controls. By analyzing ocular behaviors such as saccade velocity, fixation duration, and pupil diameter, we aim to determine how these metrics reflect executive functioning and attentional control.
Methods
A sample of 95 participants with epilepsy and 34 healthy controls completed the TMT while their eye movements were recorded. Partial correlations, controlling for age, sex, education, medication count, seizure status and epilepsy duration, examined associations between eye tracking measures and cognitive performance derived from EpiTrack and TMT performance.
Results
In the patient group, faster TMT-A performance was associated with shorter fix- ation durations (r = 0.31, p = 0.006). Lower minimum saccade velocity correlated with slower performance on both TMT-A (r = −0.35, p = 0.002) and TMT-B (r = −0.40, p<0.001), whereas higher peak saccade velocities were linked to worse performance (TMT-A: r = 0.45, p<0.001; TMT-B: r = 0.41, p<0.001). Pupil diameter findings indicated that slower TMT performance was associated with smaller minimum pupil sizes (r = −0.23 to r = −0.36), wich may indicate increased cognitive effort and attentional load. Higher EpiTrack scores also correlated with a smaller minimum pupil diameter − but only during the more demanding TMT-B − and with a more restricted saccade velocity range, reflecting greater motor control and attentional stability. No significant correlations emerged within the control group.
Conclusion
These findings highlight the potential of eye tracking as a non-invasive tool for assessing cognitive function in epilepsy. Efficient cognitive performance was characterized by stable and controlled eye movements, whereas impaired performance involved erratic saccade dynamics and prolonged fixations. Importantly, eye tracking parameters provide additional information beyond simple speed measurements, potentially enhancing the differential diagnostic capabilities of the TMT in epilepsy. The observed associations between oculomotor parameters and cognitive performance were not present in the control group, suggesting that these relationships may be specific to epilepsy. Future research should investigate whether both basic and advanced metrics of search strategies are sensitive to disease dynamics and treatment effects in epilepsy.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Objective
This study investigates the relationship between eye tracking parameters and cognitive performance during the Trail Making Test (TMT) in individuals with epilepsy and healthy controls. By analyzing ocular behaviors such as saccade velocity, fixation duration, and pupil diameter, we aim to determine how these metrics reflect executive functioning and attentional control.
Methods
A sample of 95 participants with epilepsy and 34 healthy controls completed the TMT while their eye movements were recorded. Partial correlations, controlling for age, sex, education, medication count, seizure status and epilepsy duration, examined associations between eye tracking measures and cognitive performance derived from EpiTrack and TMT performance.
Results
In the patient group, faster TMT-A performance was associated with shorter fix- ation durations (r = 0.31, p = 0.006). Lower minimum saccade velocity correlated with slower performance on both TMT-A (r = −0.35, p = 0.002) and TMT-B (r = −0.40, p<0.001), whereas higher peak saccade velocities were linked to worse performance (TMT-A: r = 0.45, p<0.001; TMT-B: r = 0.41, p<0.001). Pupil diameter findings indicated that slower TMT performance was associated with smaller minimum pupil sizes (r = −0.23 to r = −0.36), wich may indicate increased cognitive effort and attentional load. Higher EpiTrack scores also correlated with a smaller minimum pupil diameter − but only during the more demanding TMT-B − and with a more restricted saccade velocity range, reflecting greater motor control and attentional stability. No significant correlations emerged within the control group.
Conclusion
These findings highlight the potential of eye tracking as a non-invasive tool for assessing cognitive function in epilepsy. Efficient cognitive performance was characterized by stable and controlled eye movements, whereas impaired performance involved erratic saccade dynamics and prolonged fixations. Importantly, eye tracking parameters provide additional information beyond simple speed measurements, potentially enhancing the differential diagnostic capabilities of the TMT in epilepsy. The observed associations between oculomotor parameters and cognitive performance were not present in the control group, suggesting that these relationships may be specific to epilepsy. Future research should investigate whether both basic and advanced metrics of search strategies are sensitive to disease dynamics and treatment effects in epilepsy.
This study investigates the relationship between eye tracking parameters and cognitive performance during the Trail Making Test (TMT) in individuals with epilepsy and healthy controls. By analyzing ocular behaviors such as saccade velocity, fixation duration, and pupil diameter, we aim to determine how these metrics reflect executive functioning and attentional control.
Methods
A sample of 95 participants with epilepsy and 34 healthy controls completed the TMT while their eye movements were recorded. Partial correlations, controlling for age, sex, education, medication count, seizure status and epilepsy duration, examined associations between eye tracking measures and cognitive performance derived from EpiTrack and TMT performance.
Results
In the patient group, faster TMT-A performance was associated with shorter fix- ation durations (r = 0.31, p = 0.006). Lower minimum saccade velocity correlated with slower performance on both TMT-A (r = −0.35, p = 0.002) and TMT-B (r = −0.40, p<0.001), whereas higher peak saccade velocities were linked to worse performance (TMT-A: r = 0.45, p<0.001; TMT-B: r = 0.41, p<0.001). Pupil diameter findings indicated that slower TMT performance was associated with smaller minimum pupil sizes (r = −0.23 to r = −0.36), wich may indicate increased cognitive effort and attentional load. Higher EpiTrack scores also correlated with a smaller minimum pupil diameter − but only during the more demanding TMT-B − and with a more restricted saccade velocity range, reflecting greater motor control and attentional stability. No significant correlations emerged within the control group.
Conclusion
These findings highlight the potential of eye tracking as a non-invasive tool for assessing cognitive function in epilepsy. Efficient cognitive performance was characterized by stable and controlled eye movements, whereas impaired performance involved erratic saccade dynamics and prolonged fixations. Importantly, eye tracking parameters provide additional information beyond simple speed measurements, potentially enhancing the differential diagnostic capabilities of the TMT in epilepsy. The observed associations between oculomotor parameters and cognitive performance were not present in the control group, suggesting that these relationships may be specific to epilepsy. Future research should investigate whether both basic and advanced metrics of search strategies are sensitive to disease dynamics and treatment effects in epilepsy.
Greß, Hannah; Demidova, Elena; Meier, Michael; Krüger, Björn
SecureNeuroAI: Advanced Security Framework for AI-Powered Multimodal Real-Time Detection of Medical Seizure Events Proceedings Article
In: Ohm, Marc (Ed.): Proceedings of the 15th graduate workshop of the special interest group Security - Intrusion Detection and Response (SIDAR) of the German Informatics Society (GI) (SPRING 2025), pp. 22-24, GI SIG SIDAR, Nuremberg, April, 2025, ISSN: 2190-846X.
@inproceedings{Greß2025,
title = {SecureNeuroAI: Advanced Security Framework for AI-Powered Multimodal Real-Time Detection of Medical Seizure Events},
author = {Hannah Greß and Elena Demidova and Michael Meier and Björn Krüger},
editor = {Marc Ohm},
url = {https://fg-sidar.gi.de/publikationen/sidar-reports},
issn = {2190-846X},
year = {2025},
date = {2025-05-12},
urldate = {2025-05-12},
booktitle = { Proceedings of the 15th graduate workshop of the special interest group Security - Intrusion Detection and Response (SIDAR) of the German Informatics Society (GI) (SPRING 2025)},
pages = {22-24},
publisher = {GI SIG SIDAR},
address = {Nuremberg, April},
abstract = {In today's interconnected world, medical devices are increasingly equipped with novel digital technologies and AI-powered methods to improve the users' quality of life.
Despite the increased possibilities and features these devices offer due to the technical progress, cyberattacks on medical devices will increase as well with possibly severe outcomes for the patients.
At the same time, AI-based technologies could help to detect and mitigate these attacks on medical systems and their data in real-time.
Therefore, our project "SecureNeuroAI" aims to detect epileptic seizures using multimodal sensor data and AI models while also considering possible cyberattacks on this system resulting in an IT-secure system.
Our results will serve as an example for future AI-supported medical devices and systems to enhance their security and to strengthen their trustworthiness towards their (future) users.},
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In today's interconnected world, medical devices are increasingly equipped with novel digital technologies and AI-powered methods to improve the users' quality of life.
Despite the increased possibilities and features these devices offer due to the technical progress, cyberattacks on medical devices will increase as well with possibly severe outcomes for the patients.
At the same time, AI-based technologies could help to detect and mitigate these attacks on medical systems and their data in real-time.
Therefore, our project "SecureNeuroAI" aims to detect epileptic seizures using multimodal sensor data and AI models while also considering possible cyberattacks on this system resulting in an IT-secure system.
Our results will serve as an example for future AI-supported medical devices and systems to enhance their security and to strengthen their trustworthiness towards their (future) users.
Despite the increased possibilities and features these devices offer due to the technical progress, cyberattacks on medical devices will increase as well with possibly severe outcomes for the patients.
At the same time, AI-based technologies could help to detect and mitigate these attacks on medical systems and their data in real-time.
Therefore, our project "SecureNeuroAI" aims to detect epileptic seizures using multimodal sensor data and AI models while also considering possible cyberattacks on this system resulting in an IT-secure system.
Our results will serve as an example for future AI-supported medical devices and systems to enhance their security and to strengthen their trustworthiness towards their (future) users.
Khan, Umar; Riaz, Qaiser; Hussain, Mehdi; Zeeshan, Muhammad; Krüger, Björn
Towards Effective Parkinson’s Monitoring: Movement Disorder Detection and Symptom Identification Using Wearable Inertial Sensors Journal Article
In: Algorithms, vol. 18, no. 4, 2025, ISSN: 1999-4893.
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title = {Towards Effective Parkinson’s Monitoring: Movement Disorder Detection and Symptom Identification Using Wearable Inertial Sensors},
author = {Umar Khan and Qaiser Riaz and Mehdi Hussain and Muhammad Zeeshan and Björn Krüger},
url = {https://www.mdpi.com/1999-4893/18/4/203},
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year = {2025},
date = {2025-04-04},
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journal = {Algorithms},
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abstract = {Parkinson’s disease lacks a cure, yet symptomatic relief can be achieved through various treatments. This study dives into the critical aspect of anomalous event detection in the activities of daily living of patients with Parkinson’s disease and the identification of associated movement disorders, such as tremors, dyskinesia, and bradykinesia. Utilizing the inertial data acquired from the most affected upper limb of the patients, this study aims to create an optimal pipeline for Parkinson’s patient monitoring. This study proposes a two-stage movement disorder detection and classification pipeline for binary classification (normal or anomalous event) and multi-label classification (tremors, dyskinesia, and bradykinesia), respectively. The proposed pipeline employs and evaluates manual feature crafting for classical machine learning algorithms, as well as an RNN-CNN-inspired deep learning model that does not require manual feature crafting. This study also explore three different window sizes for signal segmentation and two different auto-segment labeling approaches for precise and correct labeling of the continuous signal. The performance of the proposed model is validated on a publicly available inertial dataset. Comparisons with existing works reveal the novelty of our approach, covering multiple anomalies (tremors, dyskinesia, and bradykinesia) and achieving 93.03% recall for movement disorder detection (binary) and 91.54% recall for movement disorder classification (multi-label). We believe that the proposed approach will advance the field towards more effective and comprehensive solutions for Parkinson’s detection and symptom classification.},
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Parkinson’s disease lacks a cure, yet symptomatic relief can be achieved through various treatments. This study dives into the critical aspect of anomalous event detection in the activities of daily living of patients with Parkinson’s disease and the identification of associated movement disorders, such as tremors, dyskinesia, and bradykinesia. Utilizing the inertial data acquired from the most affected upper limb of the patients, this study aims to create an optimal pipeline for Parkinson’s patient monitoring. This study proposes a two-stage movement disorder detection and classification pipeline for binary classification (normal or anomalous event) and multi-label classification (tremors, dyskinesia, and bradykinesia), respectively. The proposed pipeline employs and evaluates manual feature crafting for classical machine learning algorithms, as well as an RNN-CNN-inspired deep learning model that does not require manual feature crafting. This study also explore three different window sizes for signal segmentation and two different auto-segment labeling approaches for precise and correct labeling of the continuous signal. The performance of the proposed model is validated on a publicly available inertial dataset. Comparisons with existing works reveal the novelty of our approach, covering multiple anomalies (tremors, dyskinesia, and bradykinesia) and achieving 93.03% recall for movement disorder detection (binary) and 91.54% recall for movement disorder classification (multi-label). We believe that the proposed approach will advance the field towards more effective and comprehensive solutions for Parkinson’s detection and symptom classification.
Greß, Hannah; Alouardani, Saied; Hoffmann, Nico; Trebing, Pia; Becker, Albert J.; Surges, Rainer; Pitsch, Julika; Krüger, Björn
Digitale Transformation des Blutprobenmanagements Conference
Dreiländertagung Epilepsie 2025, 2025.
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Haaga, Lisa; Jansen, Anna; Steininger, Melissa; Müllers, Johannes; Bausch, Marcel; Jordan, Arthur; Surges, Rainer; Krüger, Björn
EpiEye – Einfluss anfallssupressiver Medikamente auf Augenbewegungen und autonome Veränderungen bei Epilepsien Conference
Dreiländertagung Epilepsie 2025, 2025.
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Müllers, Johannes; Greß, Hannah; Weber, Christian; Nadeem, Mubaris; Hütwohl, Daniela; Pukropski, Jan; Grond, Martin; Surges, Rainer; Krüger, Björn
Aufbau eines epileptologischen Telekonsils zwischen dem Klinikum Siegen und dem Universitätsklinikum Bonn Conference
Dreiländertagung Epilepsie 2025, 2025.
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booktitle = {Dreiländertagung Epilepsie 2025},
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Mustafa, Sarah Al-Haj; Jansen, Anna; Surges, Rainer; Helmstaedter, Christoph; von Wrede, Randi; Krüger, Björn
ANNE – Augen-Tracking zur Erkennung von Nebenwirkungen bei Epilepsie Conference
Dreiländertagung Epilepsie 2025, 2025.
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