Physip technology

Objective – MEEGASAFE project (Monitoring EEG for the evAluation of Somnolence to improve SAFEty) aims to better understand and better measure drowsiness, in order to improve its management and ultimately to reduce the risk of accident.

Every one of us knows what drowsiness is and has experienced it. Days that last too long, nights that are too short, irregular hours, medication, vigilance disorders: no one has escaped the stupor that seizes you at the wheel, in meetings or sitting at one’s desk. We know that drowsiness is a deterioration in the state of vigilance, between wakefulness and sleep, that it plays a part in regulating sleep, and that battling against it can be difficult. It has been clearly determined that it severely impacts performance, that it is responsible for 33% of motorway deaths, that tiredness has been identified as a major risk by the American National Transportation Safety Board, that drowsiness at the wheel costs the United States $12 billion a year, and that it has played a part in some of the most serious industrial and ecological accidents such as Chernobyl, and great oil spills such as Exxon Valdez.

And yet, it is still not very well understood which factors influence or modify our level of drowsiness. We are not able to predict the level of drowsiness that will be experienced and how it will change over time based on an instantaneous measurement. Real‐time assessment of drowsiness is still difficult to perform, and simple, reliable tools are lacking.

Support – MEEGASAFE has received the financial support of the ANR via the French LabCom program. This project has been doubly certified, by the Cap Digital and Medicen competitiveness clusters.

Partners – GENPPHAASS (attention, sleep and drowsiness neuro‐psycho‐pharmacological study group) is part of the USR 3413 Sleep Addiction and Neuropsychiatry (SANPSY) Service and Research Unit attached to the CNRS, to the University of Bordeaux (UB), and to the Pellegrin University Hospital, Bordeaux. The major focus of research for GENPPHAASS is to identify the electrophysiological and behavioral determining factors explaining vulnerability to drowsiness and to inattention. Jacques Taillard, Cécile Klochendler worked on the project.

Objective – The objective of matEEG was to develop an innovative electroencephalogram (EEG) sensor, making it possible to remove the main obstacles to a wide use, in real life, of current EEG sensors, i.e. a robust sensor that can be integrated into head equipment.

In the man‐machine couple, there is paradoxically a lack of information on the human element of the system. As machines and automatisms become more efficient, the need to measure and interpret the state and behavior of an operator, pilot, driver, worker, soldier becomes more interesting in order to understand and control the entire system. The operator’s condition monitoring, or Crew Monitoring System, aims to analyze and assess the condition of an operator in actual operating conditions. From this perspective, brain activity is a particularly relevant signal. However, in the current state of technology, EEG is mostly a laboratory signal, unusable in operational situations because the signal provided is very vulnerable to artifacts. Moreover, current sensors are difficult to handle.

A sensor capable of providing a robust signal that is easy to integrate into head equipment enables to address many applications, well beyond the application targeted in the initial project: aeronautics, railways, maritime, automotive. It would also constitute a very strong leverage effect on all research on brain activity by facilitating, and therefore generalizing, the use of EEG.

Support – The MatEEG project was supported by the French MOD, Thales and Dassault, as part of the Man Machine Teaming call for projects https://man‐machine‐teaming.com/.

Partners – The project was carried out under the technical supervision of the companies Thales and Dassault, led by Physip, with the collaboration of the company Brain Products.

Objective – The goal of COGNISIM (COGNItive load assessment to optimize SIMulation‐based military training) was to develop an automatic assessment of cognitive load from the analysis of brain activity. The project was designed to optimize simulation‐based military training. The objective was to be able, during training, to identify the strengths and weaknesses of a trainee, to personalize his training or to validate his level of mastery of operational tasks with the evolution of their cognitive load. The assessment was based on the automatic analysis of brain activity captured by the electroencephalogram (EEG). Combining signal processing and artificial intelligence, the technology makes it possible to reduce the number of sensors to allow out‐of‐the‐lab, real‐life applications. The COGNISIM use case was tank turret shooting training.

Support – COGNISIM is a RAPID Project, supported by the French MOD (Innovation Defense Agency, DGA\IDA). Emmanuel Gardinetti, in charge of Human Factor Research at the IDA, was the technical pilot for this project.

Partners – Working on cognitive performance imposed to imagine a complex system, allowing the synchronized recording of multiple physiological signals, in relation to cognitive performance evaluated through a simulator. The design and realization of the recording system brought together all the partners and various skills to produce exceptional data. COGNISIM, led by Physip, brought together a team involving the following contributors:

• Inserm Unit U1028, Lyon Neuroscience Research Center, Jérémie Mattout, Gaëtan Sanchez, Emmanuel Maby, worked on the project, on the cognitive performance protocol and on the synchronization of the EEG signal during simulation trainings.
• Agueris, a company specialized in digital military simulation, Régis Lacombe, Bernard Langlais, Youri Dmitriev, Jean‐Marie Souffez, Simone Coccia, worked on the project, by customizing the turret simulator in order to allow the physiological and behavioral recordings.
• Forvia (formerly Faurecia), International group in the automotive field, Anne‐Isabelle Da Costa, Laurent Chabert took part in the project on physiological recordings using embedded sensors.

Objectives – After a successful validation in healthy subjects and several years of algorithm development, the study aimed at validating the performance of the ASEEGA® technology in adult patients diagnosed with various sleep disorders. The study compared the visual analysis of 2 independent expert clinicians from 2 sleep centers with automated analysis. Sleep recordings were included of 95 patients diagnosed with insomnia, idiopathic hypersomnia, narcolepsy, and
obstructive sleep apnea.

Partners – Sleep recordings and one of the visual analyses of PSGs were conducted by the teams of the Functional Neurology Unit of the Wertheimer Hospital and the Center for Sleep Medicine and Respiratory Diseases of the Croix‐Rousse Hospital, in Lyon. The other visual analyses of PSGs were conducted by the teams of the CNRS‐USR 3413 SANPSY, Sleep, Addiction and Neuropsychiatry, from Bordeaux.

Support – The study was partly supported by Paris Region Entreprises funding.

The study was published in the Journal of Clinical Sleep Medicine, in 2021:
Peter‐Derex L, Berthomier C, Taillard J, Berthomier P, Bouet R, Mattout J, Brandewinder M, Bastuji H.
Automatic analysis of single‐channel sleep EEG in a large spectrum of sleep disorders. J Clin Sleep
Med. 2021;17(3):393–402.

Objectifve – The SCOAL project was pursued to better understand sleep disorders (changes in the macro and micro architecture of sleep) that are a precursor of Mild Cognitive Impairments and their potential contribution to a progressive deterioration of cognitive function.  For Physip, the project involved looking for early diagnostic and prognostic markers for cognitive decline and Alzheimer’s Disease.

Partners – The project leader was the CNRS/ Bordeaux University Unit Sanpsy with Patricia Sagaspe and Jacques Taillard notably. The project partners, besides Physip, were:

• the ISPED research unit
• the CMRR Nice medical center
• Immersion (company)

Support – SCOAL was supported by the MALZ ANR program.

Results – Results were presented in the ESRS congress in 2014 in Tallinn.

Sagaspe P, Taillard J, Chaufton C, Berthomier C, Brandewinder M, Amiéva H, Dartigues J-F, Philip P. Polysomnographic data in patients with isolated memory complaints or mild cognitive impairment. J. Sleep Res., 23 Suppl. 1:240, 2014

Objective – MEEGAPERF aims to identify automatically and in real time, EEG markers indicating a risk of impaired cognitive and physical performance. The project consists of recording the EEG of subjects involved in physical and cognitive tasks conducive to a reduction or breakdown in performance, in order to identify the cerebral signs making it possible to predict theses performance breakdowns.

Support – MEEGAPERF is a RAPID Project, supported by the French MOD (DGA) and DGE. Didier Bazalgette, in charge of Human Factor Research at the DGA, was the technical pilot for this project.

Partners – Working on physical performance imposed to imagine a complex system, allowing the synchronized recording of multiple physiological signals, in relation to physical performance evaluated through the pedaling frequency on a cycle ergometer. The design and realization of the recording system brought together all the partners and various skills to produce exceptional data.
MEEGAPERF, led by Physip, brought together a team involving the following contributors:  

• Inserm Unit U1028, CRNL (Lyon Neuroscience Research Center): Jérémie Mattout, Aline Bompas, Emmanuel Maby, Patrick Bouchet, worked on the project, on the cognitive performance aspects and on the recording of the EEG signal during physical performance

• The LIBM (ex-CRIS, Inter-University Laboratory of Human Movement Biology): Christian Collet, Vincent Pialoux, Michel Clémençon, Thomas Creveaux on physical performance

• The Signal and Image Processing Department of Telecom ParisTech: Slim Essid, Cecilia Damon, François Rigaud, Alexis Benichoux worked on original artefact processing methods

Objective – To develop an automatic method for analyzing sleep pressure, measured using the Karolinska Drowsiness Test (KDT), based on an expert artifact rejection solution.

Partners – MEEGAFIT® was validated with the CNRS/ Bordeaux University Unit Sanpsy, with Jacques Taillard.

Support –  The project was supported by  BPI and Paris Region Entreprises.

Results – An innovative automated method for artifact rejection was developed and validated. It allows fast, objective and reproducible preprocessing of data before the power spectrum is calculated on the 6‑9 Hz frequency band.

Results were presented in oral communication in Le Congrès du Sommeil (the Annual Meeting of the French Sleep Society) in Marseille in 2013.

Objectives – DAM originally aimed to evaluate the feasibility of an automated and real-time measurement of drowsiness based on a single EEG channel. It became a full development study part of the way through, given the excellent preliminary results.

Partners – Dr. Alain Muzet and Forenap participated in the DAM project, for the recording of data in a driving simulator, and visual expertise of EEG recordings.

Support – DAM was supported by the French Ministry of Defense (DGA).

Results – The DAM project resulted in the DAM algorithm, capable of describing automatically and in real-time the level of drowsiness of an active subject, on a scale ranging from 0 – full alertness, to 4 heavy drowsiness.

Results were presented in ESRS congress and in Le Congrès du Sommeil (the Annual Meeting of the French Sleep Society) in 2008.

Berthomier C, Muzet A, Berthomier P, Prado J, Mattout J, Real-Time Automatic Measurement of Drowsiness based on a Single EEG Channel

Objectives – The study aimed to validate the performance of the ASEEGA® technology, by comparing the visual analysis of 2 expert clinicians, and automated analysis, on 15 PSG recordings on healthy subjects.

Partners – Sleep recordings and visual analysis of PSGs were conducted by the team of the Functional Exploration Department of the Henri Mondor University Hospital: Prof. Marie-Pia d’Ortho, Dr. Xavier Drouot, Dr. Maria Stoïca, RPSGT Djibril Bokar-Thire.

Support – The study was partly supported by Paris Region Entreprises funding.

A detailed evaluation of the performance of the ASEEGA® technology was published in the journal Sleep, in 2007.

Automatic analysis of single-channel sleep EEG: validation in healthy individuals. Berthomier C, Drouot X, Herman-Stoïca M, Berthomier P, Prado J, Bokar-Thire D, Benoit O, Mattout J, d’Ortho MP. Sleep. 2007 Nov;30(11):1587-95.