Classification of Mental Workload Spatial Effects using Riemannian Manifold

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.

Laurent U Perrinet

Researcher in Computational Neuroscience

My research interests include Machine Learning and computational neuroscience applied to Vision.