Abstract
This study investigates the use of Riemannian geometry to detect and monitor physiological states such as mental workload (MWL) from an EEG dataset collected in an aeronautical context. The analysis, based on EEG data recorded from 14 participants performing a Simon’s task after inducing MWL by the Multi Attribute Task Battery-II (MATB), aimed to differentiate low and high workload conditions while tracking MWL effect over time. Using covariance matrices and a Minimum Distance to Mean classifier, Temporal Generalization Method (TGM) was used to assess stable decoding performance, indicating a consistent neural signature of MWL throughout the trials. Results demonstrate spatial effects of mental workload irrespective of the investigated time domain: spatial information is distributed evenly across all explored timescale.
Laurent U Perrinet
Researcher in Computational Neuroscience
My research interests include Machine Learning and computational neuroscience applied to Vision.