Workload-dependent hemispheric asymmetries during the emotion-cognition interaction: A close-to-naturalistic fNIRS study
This study investigates workload-dependent hemispheric asymmetries during emotion-cognition interactions using fNIRS, revealing lateralized prefrontal processing influenced by workload and emotional speech distractions.
Nov 15, 2023
Exploring Conventional, Automated and Deep Machine Learning for Electrodermal Activity-Based Drivers’ Stress Recognition
Machine learning models, including AutoML and deep learning, effectively classify drivers' stress states based on EDA data in simulated driving scenarios.
Jan 6, 2022
Neuro-adaptive tutoring systems - Neurophysiological-based recognition of affective-emotional and cognitive states of learners for intelligent neuro-adaptive tutoring systems
A study presenting neuro-adaptive tutoring systems leveraging EEG and machine learning to monitor, predict, and adapt to learners' cognitive and emotional states.
Jul 1, 2021
Feinfühlige Technik: Wie Neuroergonomie und Brain-Computer-Interfaces in der Praxis eingesetzt werden können
Neuroergonomische Methoden zeigen Potenziale für menschgerechte Technikgestaltung und fördern Akzeptanz sowie Nutzerzentrierung in Designprozessen.
Mar 1, 2021
Combining eye tracking and physiology for detection of emotion and workload
Combining eye tracking with physiological measures like EEG and EDA can help detect cognitive workload and emotional processing in real-life scenarios.
Jan 18, 2021
Our Brain in the Car - Driver State Decoding with Magnetecephalography (MEG)
In one of my PhD projects, I am investigating neuronal activation patterns during simulated driving with Magnetencephalography. I am interested in understanding and decoding cognitive processes during driving with different task difficulties (easy highway road and visually and attentionally more challenging construction work) and concurrent auditory emotional distraction of different valence (negative, neutral, and positive).
Apr 19, 2016