Measuring human experience

Psychophysical assessment tells us what a person judged. It does not tell us how that
judgement formed, and it cannot be run continuously without interrupting the
experience it is meant to measure. Neurophysiological measurement reaches the process
but not the verdict. We work with both, because the ground truth for data-driven
models of perception and cognition is scarce and ambiguous enough that one method
rarely settles a question on its own.

Our laboratory combines standardised psychophysical procedures and subjective testing
with EEG, EMG, eye tracking, pupillometry, skin conductance and heart rate. The human
brain is a noisy place, and recovering neural dynamics from an electroencephalogram
calls for adaptive and stochastic signal processing matched to the task, since the
relevant neural signatures depend on the perceptual or cognitive demand and are
stochastic in themselves. Acquisition is slow and expensive, so we developed a
realistic simulation of EEG scalp data from MRI-based forward models with biologically
plausible signals and noise, which incorporates individual variability and lets us
test an analysis before anyone has been recorded.

Y. Chen, T. Stephani, M. T. Bagdasarian, A. Hilsmann, P. Eisert, A. Villringer, S. Bosse, M. Gaebler, and V. V. Nikulin. ‘Realness of Face Images Can Be Decoded from Non-Linear Modulation of EEG Responses’. In: Scientific Reports 14.1 (Mar. 7, 2024), p. 5683

B. Nierula, M. T. Lafci, A. Melnik, M. Akgül, F. T. Siewe, and S. Bosse. ‘Differential Physiological Responses to Proxemic and Facial Threats in Virtual Avatar Interactions’. In:2025 IEEE International Symposium on Mixed and Augmented Reality  (ISMAR). IEEE, 2025, pp. 162–167

B. Nierula, A. Melnik, F. T. Barthel, A. Brama, A. Hilsmann, P. Eisert, V. V. Nikulin, M. Gaebler, F. Klotzsche, Y. Chen, T. Stephani, and S. Bosse. deegFake_behavioral. G-Node Open Data, Apr. 2026

E. Barzegaran, S. Bosse, P. J. Kohler, and A. M. Norcia. ‘EEGSourceSim: A Framework for Realistic Simulation of EEG Scalp Data Using MRI-based Forward Models and Biologically Plausible Signals and Noise’. In: Journal of neuroscience methods 328 (2019), p. 108377

S. Bosse, K. Brunnström, S. Arndt, M. G. Martini, N. Ramzan, and U. Engelke. ‘A Common Framework for the Evaluation of Psychophysiological Visual Quality Assessment’. In: Quality and User Experience 4.1 (2019), p3