Computer Graphics
TU Braunschweig



This project focuses on using electroencephalography (EEG) to analyze the human visual process. Human visual perception is becoming increasingly important in the analyses of rendering methods, animation results, interface design, and visualization techniques. Our work uses EEG data to provide concrete feedback on the perception of rendered videos and images as opposed to user studies that just capture the user's response. Our results so far are very promising. Not only have we been able to detect a reaction to artifacts in the EEG data, but we have also been able to differentiate between artifacts based on the EEG response.

Example workflow: Experiment, raw data, and data evaluation.


This work is being funded by the German Science Foundation DFG under the Reinhart Koselleck Project "Immersive Digital Reality". Until 2016 it was funded by the European Research Council ERC under contract No. 256941 'Reality CG'.


Jan-Philipp Tauscher, Susana Castillo, Sebastian Bosse, Marcus Magnor:
EEG-based Analysis of the Impact of Familiarity in the Perception of Deepfake Videos
in Proc. IEEE International Conference on Image Processing (ICIP), IEEE, pp. 160-164, September 2021.

Jan-Philipp Tauscher, Alexandra Witt, Sebastian Bosse, Fabian Wolf Schottky, Steve Grogorick, Susana Castillo, Marcus Magnor:
Exploring Neural and Peripheral Physiological Correlates of Simulator Sickness
in Computer Animation and Virtual Worlds, vol. 31, no. 4-5, John Wiley & Sons, Inc., pp. e1953 ff., August 2020.
electronic ISSN: 1546-427X

Jan-Philipp Tauscher, Fabian Wolf Schottky, Steve Grogorick, Marcus Magnor, Maryam Mustafa:
Analysis of Neural Correlates of Saccadic Eye Movements
in Proc. ACM Symposium on Applied Perception (SAP), no. 17, ACM, pp. 17:1-17:9, August 2018.

Maryam Mustafa, Stefan Guthe, Jan-Philipp Tauscher, Michael Goesele, Marcus Magnor:
How Human Am I? EEG-based Evaluation of Animated Virtual Characters
in Proc. ACM Human Factors in Computing Systems (CHI), ACM, pp. 5098-5108, May 2017.

Maryam Mustafa, Marcus Magnor:
EEG Based Analysis of the Perception of Computer-Generated Faces
in Proc. European Conference on Visual Media Production (CVMP), ACM, pp. 4:1-4:10, December 2016.

Maryam Mustafa, Marcus Magnor:
ElectroEncephaloGraphics: Making Waves in Computer Graphics Research
in IEEE Computer Graphics and Applications, vol. 34, no. 6, pp. 46-56, November 2014.

Maryam Mustafa, Stefan Guthe, Marcus Magnor:
Single Trial EEG Classification of Artifacts in Videos
in ACM Transactions on Applied Perception, vol. 9, no. 3, pp. 12:1-12:15, July 2012.

Maryam Mustafa, Lea Lindemann, Marcus Magnor:
EEG Analysis of Implicit Human Visual Perception
in Proc. ACM Human Factors in Computing Systems (CHI), pp. 513-516, May 2012.

Lea Lindemann, Marcus Magnor:
Assessing the Quality of Compressed Images Using EEG
in Proc. IEEE International Conference on Image Processing (ICIP), pp. 3170-3173, September 2011.

Lea Lindemann, Stephan Wenger, Marcus Magnor:
Evaluation of Video Artifact Perception Using Event-Related Potentials
in Proc. ACM Applied Perception in Computer Graphics and Visualization (APGV), p. 5, August 2011.

Related Projects

Accelerating Photo-realistic RT

The goal of this research project is to develop and evaluate new approaches to accelerate photo-realistic ray tracing. Our focus lies on novel acceleration and denoising strategies for fast and memory-efficient photo-realistic rendering. Our research covers various topics from basic research for fast intersection tests to advanced filtering techniques of Monte Carlo simulation-based rendering.

ICG Dome

Featuring more than 10 million pixels at 120 Hertz refresh rate, full-body motion capture, as well as real-time gaze tracking, our 5-meter ICG Dome enables us to research peripheral visual perception, to devise comprehensive foveal-peripheral rendering strategies, and to explore multi-user immersive visualization and interaction.

Reality CG

Scope of "Reality CG" is to pioneer a novel approach to modelling, editing and rendering in computer graphics. Instead of manually creating digital models of virtual worlds, Reality CG will explore new ways to achieve visual realism from the kind of approximate models that can be derived from conventional, real-world imagery as input.

Video Quality Assessment

Goal of this project is to assess the quality of rendered videos and especially detect those frames that contain visible artifacts, e.g. ghosting, blurring or popping.