AEye: Artificial Intelligence for Eye Tracking Data: Deep Learning Methods for the Automated Analysis of Cognitive Processes

The goal of this Junior Research Group is the development of machine learning methods (mainly deep neural network architectures) for the analysis of eye-tracking data for different application areas such as the diagnosis of developmental language disorders, biometry, driver monitoring and adaptive e-learning.

This project is located at the Department of Computer Science at the University of Potsdam, Germany and is funded by the German Federal Ministry for Education and research. For details see  Artificial Intelligence for Eye Tracking Data and our AEye-lab repository on Github.

Project duration: 2020-2024

Principal Investigator

Prof. Dr. Lena A. Jäger

Researchers

Dr. Paul Prasse

Dr. Maja Stegenwallner-Schütz

Chiara Tschirner

David R. Reich

Shuwen Deng

Daniel Krakowczyk

Research assistants

Jakob Chwastek

Assunta Süss

🏆 Best short paper award
Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models, ETRA 2023
Daniel Krakowczyk, Paul Prasse, David R. Reich, Sebastian Lapuschkin, Tobias Scheffer and Lena A. Jäger
[http | bib| code]

pymovements: A Python package for eye movement data processing, ETRA 2023
Daniel G. Krakowczyk, David R. Reich, Jakob Chwastek, Assunta Süss, Paul Prasse, Deborah N. Jakobi, Oleksii Turuta, Paweł Kasprowski and Lena A. Jäger
[https| PyPI | |bib]

Eyettention: An Attention-based Dual-Sequence Model for Predicting Human Scanpaths during Reading, Proceedings of the ACM on Human-Computer Interaction 
Shuwen Deng, David R. Reich, Paul Prasse, Patrick Haller, Tobias Scheffer and Lena A. Jäger
[http |bib| code]

SP-EyeGAN: Generating Synthetic Eye Movement Data with Generative Adversarial Networks, ETRA 2023
Paul Prasse, David R. Reich, Silvia Makowski, Shuwen Deng, Daniel Krakowczyk, Tobias Scheffer and Lena A. Jäger
[http | bib| code]

Selection of XAI Methods Matters: Evaluation of Feature Attribution Methods for Oculomotoric Biometric Identification, NeurIPS/Gaze Meets ML 2022
Daniel Krakowczyk, David R. Reich, Paul Prasse, Sebastian Lapuschkin, Tobias Scheffer, and Lena A. Jäger
[pdf | bib]

Oculomotoric Biometric Identification under the Influence of Alcohol and Fatigue, IJCB 2022
Silvia Makowski, Paul Prasse, Lena A. Jäger, and Tobias Scheffer
[pdf | bib]

Detection of ADHD based on Eye Movements during Natural Viewing, ECML/PKDD 2022
Shuwen Deng, Paul Prasse, David R. Reich, Sabine Dziemian, Maja Stegenwallner-Schütz, Daniel Krakowczyk, Silvia Makowski, Nicolas Langer, Tobias Scheffer, and Lena A. Jäger
[pdf | video trailer | code| bib]

Inferring Native and Non-Native Human Reading Comprehension and Subjective Text Difficulty from Scanpaths in Reading, ETRA 2022
David R. Reich, Paul Prasse, Chiara Tschirner, Patrick Haller, Frank Goldhammer, and Lena A. Jäger
[http | video | bib]

Fairness in Oculomotoric Biometric Identification, ETRA 2022
Paul Prasse, David R. Reich, Silvia Makowski, Lena A. Jäger, and Tobias Scheffer
[http | video | bib]

DeepEyedentificationLive: Oculomotoric Biometric Identification and Presentation-Attack Detection using Deep Neural Networks, IEEE Transactions on Biometrics, Behavior, and Identity Science 2021
Silvia Makowski, Paul Prasse, David R. Reich, Daniel Krakowczyk, Lena A. Jäger and Tobias Scheffer
[ http | bib]

On the Relationship between Eye Tracking Resolution and Performance of Oculomotoric Biometric Identification, Procedia Computer Science 2020
Paul Prasse, Lena A. Jäger, Silvia Makowski, Moritz Feuerpfeil, and Tobias Scheffer
[ http | bib]

Discriminative Viewer Identification using Generative Models of Eye Gaze, Procedia Computer Science 2020
Silvia Makowski, Lena A. Jäger, Lisa Schwetlick, Hans Trukenbrod, Ralf Engbert, and Tobias Scheffer
[ http | bib]

Biometric Identification and Presentation-Attack Detection using Micro- and Macro-Movements of the Eyes, IJCB 2020
Silvia Makowski, Lena A. Jäger, Paul Prasse, and Tobias Scheffer
[ pdf | http | bib]