Un recente studio ha mostrato che il carico cognitivo e lo stress mentale variano a seconda dello spazio di lavoro.

In particolare, gli open-space tanto amati da molti datori di lavoro costringono il cervello a filtrare continuamente gli stimoli esterni a discapito delle prestazioni e dell'efficacia dei processi cognitivi.

Sarร  arrivato il momento di ripensare agli spazi lavorativi?

https://www.rivistastudio.com/uffici-open-space-problemi/

#work #workspace #spazi #lavoro #mentalworkload #caricocognitivo

Secondo una ricerca scientifica gli uffici open space fanno male al cervello, fanno stancare di piรน e lavorare peggio | Rivista Studio

A quanto pare ci voleva una ricerca per capire che rumore continuo, confusione incessante e assenza di spazio personale non fanno bene al cervello.

Rivista Studio

This year at #CCN2025 we will be showcasing our research on the classification of Mental Workload ๐Ÿฅต Spatial Effects using Riemannian Manifold.

๐Ÿ“… When: Wednesday, August 13, 1:00 โ€“ 4:00 pm
๐Ÿ“ Where: CCN 2025 Conference Venue, de Brug & E-Hall
๐Ÿ“‹ 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! ๐Ÿš€

https://laurentperrinet.github.io/publication/choplin-25-ccn/

๐Ÿ‘ CNRS @cnrs - Aix-Marseille University - ONERA, The French Aerospace Lab CNRS

#CCN2025 #Mental #Workload #MentalWorkload #Riemannian #Manifold

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 information is distributed evenly across all explored frequency bands.

Next-generation neural computations