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Poster Session B: Wednesday, August 13, 1:00 – 4:00 pm, de Brug & E‑Hall

Classification of Mental Workload Spatial Effects using Riemannian Manifold

Agathe CHOPLIN¹, Thomas Rakotomamonjy², Laurent Perrinet³, Nicolas Lantos², Sébastien Angelliaume²; ¹Université d'Aix-Marseille, ²Onera - The french aerospace lab, ³Aix Marseille Univ

Presenter: Agathe CHOPLIN

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.

Topic Area: Memory, Spatial Cognition & Skill Learning

Extended Abstract: Full Text PDF