Tootfinder

🧠 TODAY at #CCN2025 ! Poster A145, 1:30-4:30pm at de Brug & E‑Hall. We've developed a bio-inspired "What-Where" CNN that mimics primate visual pathways - achieving better classification with less computation. Come chat! 🎯
Presented by main author Jean-Nicolas JÉRÉMIE and in cosupervision with Emmanuel Daucé

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, offerin…

Classification of Mental Workload Spatial Effects using Riemannian Manifold | Next-generation neural computations
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…