Abstract
This study investigates the use of Riemannian geometry to classify mental workload from an EEG dataset collected in an aeronautical context. The analysis, based on EEG data recorded from 16 participants performing a Simon task, aimed to differentiate low and high workload conditions. Using covariance matrices and a Minimum Distance to Mean (MDM) classifier, the results demonstrate spatial effects of mental workload irrespective of the investigated spectral domain. This demonstrates that spatial information is distributed evenly across all explored frequency bands.
This year at #CCN2025 we will be showcasing our research on the classification of Mental Workload 🥵 Spatial Effects using Riemannian Manifold.
📅 When: Wednesday, August 13, 1:00 – 4:00 pm 📍 Where: CCN 2025 Conference Venue, de Brug & E-Hall 📋 What: Poster B152
- It leverages advanced mathematical techniques to better understand and classify mental workloads, offering new insights into cognitive processes and potential applications in various fields such as neuroscience, psychology, and human-computer interaction.
- By utilizing Riemannian geometry, this research provides a robust framework for analyzing spatial effects in mental workload, paving the way for more accurate and efficient classification methods. This contribution not only advances our theoretical understanding but also has practical implications for improving mental workload assessment and management.
See you there! 🚀
https://laurentperrinet.github.io/publication/choplin-25-ccn/
👏 CNRS @cnrs@social.numerique.gouv.fr - Aix-Marseille University - ONERA, The French Aerospace Lab CNRS
#CCN2025 #Mental #Workload #MentalWorkload #Riemannian #Manifold
Links:
Laurent U Perrinet
Researcher in Computational Neuroscience
My research interests include Machine Learning and computational neuroscience applied to Vision.