Monitoring by EEG for the evAluation of Somnolence to improve SAFEty – MEEGASAFE

Drowsiness is an intermediate state between wake and sleep that induces an instable wake state and impairs motor and cognitive performances. Drowsiness has been identified as a major cause of traffic, professional and domestic accidents, associated with human, economic and social consequences. Even if detection and warning systems exist to identify hypovigilance at the wheel, they are not transferable to other environments and lack precision. Thanks to recent technological innovations in the field of electroencephalogram recording and sensors, EEG as a measure of drowsiness becomes accessible in everyday situations. Car and plane manufacturers have started working on EEG-based monitoring solutions.
The Groupe d'Etude Neuro-Psycho-PHarmacologique de l'Attention du Sommeil et de la Somnolence (GENPPHAASS) from the USR 3413 SANSPSY and the Physip Company will bring their complementary expertise together in the Common Lab MEEGASAFE. Physip will bring its solutions for the analysis of drowsiness based on a reduced number of EEG sensors, GENPPHAASS will bring its expertise and unique experimental means (evaluation platform and Equipex Phenovirt). The objective is to develop solutions for the evaluation and management of drowsiness, based on 2 EEG sensors only, aiming at reducing the risk of accidents. Targeted applications are multiple: transports (air, rail, sea, road), air traffic control, military, medical.
The scientific and technical program of MEEGASAFE falls into 5 axes:
Axis 1 Data, aims at building a unique dataset, made of EEG recording under various controlled conditions with regards to phenotype, wake duration, time of day, tasks (cognitive and operational tasks). Axes 2, 3 and 4 will rely on data collected in Axis 1.
Axis 2 Analysis Methods, aims at improving the performance of existing methods for the automated analysis of wake EEG developed by Physip, in order to allow real time evaluation of drowsiness based on 2 EEG sensors only, capable of addressing inter subject variability and real life recording conditions.
Axis 3 Explanatory and predictive model of drowsiness, aims at improving the understanding of what conditions determine somnolence and at predicting the evolution of the level of drowsiness based on complex predictive methods. These analyses will be based on data collected in Axis 1.
Axis 4: Explanatory and predictive model of cognitive and operational performance, aims at evaluating and modelling the impact of drowsiness on performance and its evolution by associating a given level of drowsiness and performance while performing various tasks. Eventually, it aims at estimating a risk of impaired performance based on the evaluation of a level of drowsiness. These analyses will be based on data collected in Axis 1. These complex explanatory and predictive modeling (Axis 3 and 4) will be worked with AI tools
Axis 5 Measurement tools, aims at developing solutions based on off-the-shelve products optimized to address out-of the lab EEG recording, to be used in everyday situations, proposing simple metrics, manageable, robust and easy-to-use systems implementing easy EEG device, automated analysis and predictive model.
Expected results of MEEGASAFE have a huge scientific and commercial potential. MEEGASAFE will allow a better understanding of the determinants of drowsiness and its impact on performance and the development of methods and models for the evaluation and prediction of somnolence and associated performance and easy-to-use evaluation kits. These results are intended to be exploited as expert service by the partners of the Common Lab, and implemented as technology transfer by industrial clients.