Vers l’application de l’apprentissage par renforcement inverse aux réseaux naturels d’attention

Le cerveau humain, pour allouer de manière optimale les ressources attentionnelles limitées dont il dispose, supprime ou renforce l’activation de circuits neuronaux : il implémente des heuristiques. Dans une approche novatrice, nous proposons d’utiliser l’apprentissage par renforcement inverse pour caractériser la dynamique d’activation de ces réseaux. Un protocole expérimental est proposé, et les données collectées devraient permettre, à terme, de vérifier cette démarche

Unobtrusive EEG measures of an oddball paradigm in flight simulator and real flight conditions: A case study

Piloting aircraft is a demanding task in a dynamic, uncertain environment 1. Attention distribution is a key issue for piloting, relying on a tradeoff between focused and divided attention (i.e., avoiding distraction or detecting changes). Their homeostasis may be dismissed when demand exceeds mental capacity, canceling out the processing of incoming stimuli (e.g. auditory alarms). For instance, accidents analyses disclosed evidences of inattentional deafness in which pilots failed to respond to critical auditory warnings. Experiments in flight simulators [2] and real-flight conditions [3] demonstrated the possibility of implementing an electro-encephalography (EEG)-based brain-computer interface to detect and predict the likelihood of this phenomenon. Yet, these experiments used bulky systems uncomfortable to wear over long periods of time. New portable EEG systems offer a promising avenue for implementing neuroadaptive technologies in real world settings [3]. To benchmark these systems under ecological settings, we conducted a study in simulated and real flight conditions while recording the participant’s brain activity with the cEEGrid system (TMSi, Oldenzaal, Netherlands [4])

Hyperscan Project: investigate the physiological and neural markers of teammates cooperation

Servicemen are now by far engaged in complex operations involving cooperation with multiple actors and specialists under time pressure in uncertain and highly dynamic hostile environments. Moreover, military operators will be more likely to interact with advanced artificial intelligence (AI) based technology. There is a need to implement monitoring solutions to objectively assess the efficiency of human-human and human-AI teaming on the battlefield. The Hyperscan research project forms part of this perspective and aims to investigate the physiological and neural markers of cooperation between human teammates and as well as between humans and artificial agents

AI can fool us humans, but not at the psycho-physiological level: a hyperscanning and physiological synchrony study

This study aims at investigating the neural and physiological correlates of human-human and human-AI interactions under ecological settings. We designed a scenario in which a ground controller had to guide his/her pilot to reach a location. We also implemented a Controller-Bot and a Pilot-Bot using AI techniques to behave like real human operators. The cooperation between controllers and pilots were either genuine (‘Coop scenarios’ – four missions), explicitly notified as pilot-Bot and controller-Bot interactions (‘No coop scenarios’ – two missions), or with no notification that they were actually collaborating with their AI counterparts (‘fake coop scenarios’ – two missions). Sixteen participants (8 dyads) equipped with EEG and ECG took part in this experiment. Our findings disclosed that Human-Human dyads exhibited similar performance to Human-Bots dyads whether the human participants were aware that they were playing with a bot or not. Our participants declared that they did not realize they were playing with an AI in the fake cooperation condition. These findings indicate that 1) humans can be fooled by AI, and that 2) humans can behave in a natural way with AI. Interestingly enough, our analyses revealed that the cardiac activity of controllers and pilots was more synchronized when they were collaborating together than when they were playing with AI (being aware or not). Similarly, EEG analyses disclosed a higher cerebral efficiency and connectivity between the two brains when teammates were interacting together than when cooperating with AI

Retrospective on the First Passive Brain-Computer Interface Competition on Cross-Session Workload Estimation

International audienceAs is the case in several research domains, data sharing is still scarce in the field of Brain-Computer Interfaces (BCI), and particularly in that of passive BCIs— i.e ., systems that enable implicit interaction or task adaptation based on a user's mental state(s) estimated from brain measures. Moreover, research in this field is currently hindered by a major challenge, which is tackling brain signal variability such as cross-session variability. Hence, with a view to develop good research practices in this field and to enable the whole community to join forces in working on cross-session estimation, we created the first passive brain-computer interface competition on cross-session workload estimation. This competition was part of the 3rd International Neuroergonomics conference. The data were electroencephalographic recordings acquired from 15 volunteers (6 females; average 25 y.o.) who performed 3 sessions—separated by 7 days—of the Multi-Attribute Task Battery-II (MATB-II) with 3 levels of difficulty per session (pseudo-randomized order). The data -training and testing sets—were made publicly available on Zenodo along with Matlab and Python toy code ( https://doi.org/10.5281/zenodo.5055046 ). To this day, the database was downloaded more than 900 times (unique downloads of all version on the 10th of December 2021: 911). Eleven teams from 3 continents (31 participants) submitted their work. The best achieving processing pipelines included a Riemannian geometry-based method. Although better than the adjusted chance level (38% with an α at 0.05 for a 3-class classification problem), the results still remained under 60% of accuracy. These results clearly underline the real challenge that is cross-session estimation. Moreover, they confirmed once more the robustness and effectiveness of Riemannian methods for BCI. On the contrary, chance level results were obtained by one third of the methods—4 teams- based on Deep Learning. These methods have not demonstrated superior results in this contest compared to traditional methods, which may be due to severe overfitting. Yet this competition is the first step toward a joint effort to tackle BCI variability and to promote good research practices including reproducibility

Good scientific practice in MEEG Research: Progress and Perspectives

Good Scientific Practice (GSP) refers to both explicit and implicit rules, recommendations, and guidelines that help scientists to produce work that is of the highest quality at any given time, and to efficiently share that work with the community for further scrutiny or utilization.For experimental research using magneto- and electroencephalography (MEEG), GSP includes specific standards and guidelines for technical competence, which are periodically updated and adapted to new findings. However, GSP also needs to be periodically revisited in a broader light. At the LiveMEEG 2020 conference, a reflection on GSP was fostered that included explicitly documented guidelines and technical advances, but also emphasized intangible GSP: a general awareness of personal, organizational, and societal realities and how they can influence MEEG research.This article provides an extensive report on most of the LiveMEEG contributions and new literature, with the additional aim to synthesize ongoing cultural changes in GSP. It first covers GSP with respect to cognitive biases and logical fallacies, pre-registration as a tool to avoid those and other early pitfalls, and a number of resources to enable collaborative and reproducible research as a general approach to minimize misconceptions. Second, it covers GSP with respect to data acquisition, analysis, reporting, and sharing, including new tools and frameworks to support collaborative work. Finally, GSP is considered in light of ethical implications of MEEG research and the resulting responsibility that scientists have to engage with societal challenges.Considering among other things the benefits of peer review and open access at all stages, the need to coordinate larger international projects, the complexity of MEEG subject matter, and today's prioritization of fairness, privacy, and the environment, we find that current GSP tends to favor collective and cooperative work, for both scientific and for societal reasons

Neurofunctional correlates of the out-of-the-loop phenomenon : impacts on performance monitoring

Les mutations technologiques à l’œuvre dans les systèmes aéronautiques ont profondément modifié les interactions entre l’homme et la machine. Au fil de cette évolution, les opérateurs se sont retrouvés face à des systèmes de plus en plus complexes, de plus en plus automatisés et de plus en plus opaques. De nombreuses tragédies montrent à quel point la supervision des systèmes par des opérateurs humains reste un problème sensible. En particulier, de nombreuses évidences montrent que l’automatisation a eu tendance à éloigner l’opérateur de la boucle de contrôle des systèmes, créant un phénomène dit de sortie de boucle (OOL). Ce phénomène se caractérise notamment par une diminution de la conscience de la situation et de la vigilance de l’opérateur, ainsi qu’une complaisance et une sur-confiance dans les automatismes. Ces difficultés déclenchent notamment une baisse des performances de l’opérateur qui n’est plus capable de détecter les erreurs du système et de reprendre la main si nécessaire. La caractérisation de l’OOL est donc un enjeux majeur des interactions homme-système et de notre société en constante évolution. Malgré plusieurs décennies de recherche, l’OOL reste difficile à caractériser, et plus encore à anticiper. Nous avons dans cette thèse utilisé les théories issues des neurosciences, notamment sur le processus de détection d’erreurs, afin de progresser sur notre compréhension de ce phénomène dans le but de développer des outils de mesure physiologique permettant de caractériser l’état de sortie de boucle lors d’interactions avec des systèmes écologiques. En particulier, l’objectif de cette thèse était de caractériser l’OOL à travers l’activité électroencéphalographique (EEG) dans le but d’identifier des marqueurs et/ou précurseurs de la dégradation du processus de supervision du système. Nous avons dans un premier temps évalué ce processus de détection d’erreurs dans des conditions standards de laboratoire plus ou moins complexes. Deux études en EEG nous ont d’abord permis : (i) de montrer qu’une activité cérébrale associée à ce processus cognitif se met en place dans les régions fronto-centrales à la fois lors de la détection de nos propres erreurs (ERN-Pe et FRN-P300) et lors de la détection des erreurs d’un agent que l’on supervise, (complexe N2-P3) et (ii) que la complexité de la tâche évaluée peut dégrader cette activité cérébrale. Puis nous avons mené une autre étude portant sur une tâche plus écologique et se rapprochant des conditions de supervision courantes d’opérateurs dans l’aéronautique. Au travers de techniques de traitement du signal EEG particulières (e.g., analyse temps-fréquence essai par essai), cette étude a mis en évidence : (i) l’existence d’une activité spectrale θ dans les régions fronto-centrales qui peut être assimilée aux activités mesurées en condition de laboratoire, (ii) une diminution de l’activité cérébrale associée à la détection des décisions du système au cours de la tâche, et (iii) une diminution spécifique de cette activité pour les erreurs. Dans cette thèse, plusieurs mesures et analyses statistiques de l’activité EEG ont été adaptées afin de considérer les contraintes des tâches écologiques. Les perspectives de cette thèse ouvrent sur une étude en cours dont le but est de mettre en évidence la dégradation de l’activité de supervision des systèmes lors de la sortie de boucle, ce qui permettrait d’identifier des marqueurs précis de ce phénomène permettant ainsi de le détecter, voire même, de l’anticiper.The ongoing technological mutations occuring in aeronautics have profoundly changed the interactions between men and machines. Systems are more and more complex, automated and opaque. Several tragedies have reminded us that the supervision of those systems by human operators is still a challenge. Particularly, evidences have been made that automation has driven the operators away from the control loop of the system thus creating an out-of-the-loop phenomenon (OOL). This phenomenon is characterized by a decrease in situation awareness and vigilance, but also complacency and over-reliance towards automated systems. These difficulties have been shown to result in a degradation of the operator’s performances. Thus, the OOL phenomenon is a major issue of today’s society to improve human-machine interactions. Even though it has been studied for several decades, the OOL is still difficult to characterize, and even more to predict. The aim of this thesis is to define how cognitive neurosciences theories, such as the performance monitoring activity, can be used in order to better characterize the OOL phenomenon and the operator’s state, particularly through physiological measures. Consequently, we have used electroencephalographic activity (EEG) to try and identify markers and/or precursors of the supervision activity during system monitoring. In a first step we evaluated the error detection or performance monitoring activity through standard laboratory tasks, with varying levels of difficulty. We performed two EEG studies allowing us to show that : (i) the performance monitoring activity emerges both for our own errors detection but also during another agent supervision, may it be a human agent or an automated system, and (ii) the performance monitoring activity is significantly decreased by increasing task difficulty. These results led us to develop another experiment to assess the brain activity associated with system supervision in an ecological environment, resembling everydaylife aeronautical system monitoring. Thanks to adapted signal processing techniques (e.g. trial-by-trial time-frequency decomposition), we were able to show that there is : (i) a fronto-central θ activité time-locked to the system’s decision similar to the one obtained in laboratory condition, (ii) a decrease in overall supervision activity time-locked to the system’s decision, and (iii) a specific decrease of monitoring activity for errors. In this thesis, several EEG measures have been used in order to adapt to the context at hand. As a perspective, we have developped a final study aiming at defining the evolution of the monitoring activity during the OOL. Finding markers of this degradation would allow to monitor its emersion, and even better, predict it

Corrélats neuro-fonctionnels du phénomène de sortie de boucle : impacts sur le monitoring des performances

The ongoing technological mutations occuring in aeronautics have profoundly changed the interactions between men and machines. Systems are more and more complex, automated and opaque. Several tragedies have reminded us that the supervision of those systems by human operators is still a challenge. Particularly, evidences have been made that automation has driven the operators away from the control loop of the system thus creating an out-of-the-loop phenomenon (OOL). This phenomenon is characterized by a decrease in situation awareness and vigilance, but also complacency and over-reliance towards automated systems. These difficulties have been shown to result in a degradation of the operator’s performances. Thus, the OOL phenomenon is a major issue of today’s society to improve human-machine interactions. Even though it has been studied for several decades, the OOL is still difficult to characterize, and even more to predict. The aim of this thesis is to define how cognitive neurosciences theories, such as the performance monitoring activity, can be used in order to better characterize the OOL phenomenon and the operator’s state, particularly through physiological measures. Consequently, we have used electroencephalographic activity (EEG) to try and identify markers and/or precursors of the supervision activity during system monitoring. In a first step we evaluated the error detection or performance monitoring activity through standard laboratory tasks, with varying levels of difficulty. We performed two EEG studies allowing us to show that : (i) the performance monitoring activity emerges both for our own errors detection but also during another agent supervision, may it be a human agent or an automated system, and (ii) the performance monitoring activity is significantly decreased by increasing task difficulty. These results led us to develop another experiment to assess the brain activity associated with system supervision in an ecological environment, resembling everydaylife aeronautical system monitoring. Thanks to adapted signal processing techniques (e.g. trial-by-trial time-frequency decomposition), we were able to show that there is : (i) a fronto-central θ activité time-locked to the system’s decision similar to the one obtained in laboratory condition, (ii) a decrease in overall supervision activity time-locked to the system’s decision, and (iii) a specific decrease of monitoring activity for errors. In this thesis, several EEG measures have been used in order to adapt to the context at hand. As a perspective, we have developped a final study aiming at defining the evolution of the monitoring activity during the OOL. Finding markers of this degradation would allow to monitor its emersion, and even better, predict it.Les mutations technologiques à l’œuvre dans les systèmes aéronautiques ont profondément modifié les interactions entre l’homme et la machine. Au fil de cette évolution, les opérateurs se sont retrouvés face à des systèmes de plus en plus complexes, de plus en plus automatisés et de plus en plus opaques. De nombreuses tragédies montrent à quel point la supervision des systèmes par des opérateurs humains reste un problème sensible. En particulier, de nombreuses évidences montrent que l’automatisation a eu tendance à éloigner l’opérateur de la boucle de contrôle des systèmes, créant un phénomène dit de sortie de boucle (OOL). Ce phénomène se caractérise notamment par une diminution de la conscience de la situation et de la vigilance de l’opérateur, ainsi qu’une complaisance et une sur-confiance dans les automatismes. Ces difficultés déclenchent notamment une baisse des performances de l’opérateur qui n’est plus capable de détecter les erreurs du système et de reprendre la main si nécessaire. La caractérisation de l’OOL est donc un enjeux majeur des interactions homme-système et de notre société en constante évolution. Malgré plusieurs décennies de recherche, l’OOL reste difficile à caractériser, et plus encore à anticiper. Nous avons dans cette thèse utilisé les théories issues des neurosciences, notamment sur le processus de détection d’erreurs, afin de progresser sur notre compréhension de ce phénomène dans le but de développer des outils de mesure physiologique permettant de caractériser l’état de sortie de boucle lors d’interactions avec des systèmes écologiques. En particulier, l’objectif de cette thèse était de caractériser l’OOL à travers l’activité électroencéphalographique (EEG) dans le but d’identifier des marqueurs et/ou précurseurs de la dégradation du processus de supervision du système. Nous avons dans un premier temps évalué ce processus de détection d’erreurs dans des conditions standards de laboratoire plus ou moins complexes. Deux études en EEG nous ont d’abord permis : (i) de montrer qu’une activité cérébrale associée à ce processus cognitif se met en place dans les régions fronto-centrales à la fois lors de la détection de nos propres erreurs (ERN-Pe et FRN-P300) et lors de la détection des erreurs d’un agent que l’on supervise, (complexe N2-P3) et (ii) que la complexité de la tâche évaluée peut dégrader cette activité cérébrale. Puis nous avons mené une autre étude portant sur une tâche plus écologique et se rapprochant des conditions de supervision courantes d’opérateurs dans l’aéronautique. Au travers de techniques de traitement du signal EEG particulières (e.g., analyse temps-fréquence essai par essai), cette étude a mis en évidence : (i) l’existence d’une activité spectrale θ dans les régions fronto-centrales qui peut être assimilée aux activités mesurées en condition de laboratoire, (ii) une diminution de l’activité cérébrale associée à la détection des décisions du système au cours de la tâche, et (iii) une diminution spécifique de cette activité pour les erreurs. Dans cette thèse, plusieurs mesures et analyses statistiques de l’activité EEG ont été adaptées afin de considérer les contraintes des tâches écologiques. Les perspectives de cette thèse ouvrent sur une étude en cours dont le but est de mettre en évidence la dégradation de l’activité de supervision des systèmes lors de la sortie de boucle, ce qui permettrait d’identifier des marqueurs précis de ce phénomène permettant ainsi de le détecter, voire même, de l’anticiper

Courses ISAE-SUPAERO

This is the courses that I have developped and have been giving to Engineering School Student (Bac+4 or Bac+5) and Master's degree students for a few years at ISAE-SUPAERO. Courses are on Human-Human and Human-System Interaction, Experimental design definition, Perception, EEG signal processing, ECG signal processing, etc