Real-Time State Estimation in a Flight Simulator Using fNIRS

Working memory is a key executive function for flying an aircraft. This function is particularly critical when pilots have to recall series of air traffic control instructions. However, working memory limitations may jeopardize flight safety. Since the functional near-infrared spectroscopy (fNIRS) method seems promising for assessing working memory load, our objective is to implement an on-line fNIRS-based inference system that integrates two complementary estimators. The first estimator is a real-time state estimation MACD-based algorithm dedicated to identifying the pilot’s instantaneous mental state (not-on-task vs. on-task). It does not require a calibration process to perform its estimation. The second estimator is an on-line SVM-based classifier that is able to discriminate task difficulty (low working memory load vs. high working memory load). These two estimators were tested with 19 pilots who were placed in a realistic flight simulator and were asked to recall air traffic control instructions. We found that the estimated pilot’s mental state matched significantly better than chance with the pilot’s real state (62% global accuracy, 58% specificity, and 72% sensitivity). The second estimator, dedicated to assessing single trial working memory loads, led to 80% classification accuracy, 72% specificity, and 89% sensitivity. These two estimators establish reusable blocks for further fNIRS-based passive brain computer interface development

Using near infrared spectroscopy and heart rate variability to detect mental overload

Mental workload is a key factor influencing the occurrence of human error, especially during piloting and remotely operated vehicle (ROV) operations, where safety depends on the ability of pilots to act appropriately. In particular, excessively high or low mental workload can lead operators to neglect critical information. The objective of the present study is to investigate the potential of functional Near Infrared Spectroscopy (fNIRS) – a non-invasive method of measuring prefrontal cortex activity – in combination with measurements of heart rate variability (HRV), to predict mental workload during a simulated piloting task, with particular regard to task engagement and disengagement. Twelve volunteers performed a computer-based piloting task in which they were asked to follow a dynamic target with their aircraft, a task designed to replicate key cognitive demands associated with real life ROV operating tasks. In order to cover a wide range of mental workload levels, task difficulty was manipulated in terms of processing load and difficulty of control – two critical sources of workload associated with piloting and remotely operating a vehicle. Results show that both fNIRS and HRV are sensitive to different levels of mental workload; notably, lower prefrontal activation as well as a lower LF/HF ratio at the highest level of difficulty, suggest that these measures are suitable for mental overload detection. Moreover, these latter measurements point towards the existence of a quadratic model of mental workload

EEG-engagement index and auditory alarm misperception: an inattentional deafness study in actual flight condition

The inability to detect auditory alarms is a critical issue in many do- mains such as aviation. An interesting prospect for flight safety is to understand the neural mechanisms underpinning auditory alarm misperception under actual flight condition. We conducted an experiment in which four pilots were to re- spond by button press when they heard an auditory alarm. The 64 channel Cognionics dry-wireless EEG system was used to measure brain activity in a 4 seat light aircraft. An instructor was present on all flights and in charge of initi- ating the various scenarios to induce two levels of task engagement (simple navigation task vs. complex maneuvering task). Our experiment revealed that inattentional deafness to single auditory alarms could take place as the pilots missed a mean number of 12.5 alarms occurring mostly during the complex maneuvering condition, when the EEG engagement index was high

Neural signature of inattentional deafness

Inattentional deafness is the failure to hear otherwise audible sounds (usually alarms) that may occur under high workload conditions. One potential cause for its occurrence could be an atten- tional bottleneck that occurs when task demands are high, resulting in lack of resources for processing of additional tasks. In this fMRI experiment, we explore the brain regions active during the occurrence of inattentional deafness using a difficult perceptual-motor task in which the participants fly through a simulated Red Bull air race course and at the same time push a button on the joystick to the presence of audio alarms. Participants were instructed to focus on the difficult piloting task and to press the button on the joystick quickly when they noticed an audio alarm. The fMRI results revealed that audio misses relative to hits had significantly greater activity in the right inferior frontal gyrus IFG and the superior medial frontal cortex. Consistent with an attentional bottleneck, activity in these regions was also present for poor flying performance (contrast of gates missed versus gates passed for the flying task). A psychophysiological interaction analysis from the IFG identified reduced effective connectivity to auditory processing regions in the right superior temporal gyrus for missed audio alarms relative to audio alarms that were heard. This study identifies a neural signature of inattentional deafness in an ecologically valid situation by directly measuring differences in brain activity and effective connectivity between audio alarms that were not heard compared to those that were heard

Anticipating human error before it happens: Towards a psychophysiological model for online prediction of mental workload

Mental workload is a key factor influencing the occurrence of human error; specifically in remotely-operated vehicle operations. Both low and high mental workload has been found to disrupt performance in a nonlinear fashion at a given task; however, research that has attempted to predict individual mental workload has met with little success. The objective of the present study is to investigate the potential of the dual-task paradigm and prefrontal cortex oxygenation as online measures of mental workload. Subjects performed a computerized object tracking task in which they had to follow a dynamic target with their aircraft. Task difficulty was manipulated in terms of processing load and difficulty of control: two critical sources of workload associated with remotely operating a vehicle. Mental workload was assessed by a secondary concurrent time production task and a functional near infrared spectrometer. Results show that the effects of task difficulty differ across measures of mental workload. This pattern of behavioural and neurophysiologic results suggests that the empirically-based selection of an appropriate secondary task for the measure of mental workload is critical as its sensitivity may vary considerably depending on task factors

Mesure et analyse de l’activité cérébrale par des techniques d’imagerie en proche infrarouge

La mesure de charge attentionnelle est un enjeu d’avenir dans la mise en place d’interfaces adaptatives dans le milieu aéronautique, pour améliorer les conditions d’utilisation et la sécurité (en particulier dans la détection de phénomènes attentionnels extrêmes tels que la tunnélisation ou la divagation attentionnelles). Les mesures physiologiques, par leur utilisabilité en temps réel et leur estimation objective du niveau de charge attentionnelle, sont d’un intérêt croissant pour les chercheurs. Dans ce rapport, nous étudions les différentes métriques associées à la mesure de charge attentionnelle, et expérimentons l’utilisabilité de l’imagerie par spectroscopie en proche infrarouge (une méthode d’imagerie récente) dans la détection de niveaux de charge attentionnelle en situation de pilotage. Dans ce cadre, nous avons mis en place une expérience faisant intervenir les effets de la mémoire et de la difficulté de la tâche sur la charge attentionnelle. Les résultats prometteurs de cette méthode d’imagerie sur cette expérience nous permettent d’envisager son utilisation dans le cadre d’une interface adaptative. To improve security and usability conditions in the aeronautic field, being able of monitoring user’s attentional load and using its measure in an adaptive interface is becoming a widely considered solution (especially concerning the detection of extreme attentional states such as attentional tunneling or low alertness). In this regard, physiological measures bring a real-time and objective measure of attentional load. In this report, we consider different metrics of the attentional load, and study more specifically (through experimentation) the usability of functional Near Infrared Spectroscopy (NIRS) as an assessment of pilot’s attentional workload. An experiment was settled to look at the effect of memory and task difficulty on attentional workload. The promising results of NIRS on this experiment encourage us in using it in an adaptive interface

See no Evil: Challenges of security surveillance and monitoring

While the development of intelligent technologies in security surveillance can augment human capabilities, they do not replace the role of the operator entirely; as such, when developing surveillance support it is critical that limitations to the cognitive system are taken into account. The current article reviews the cognitive challenges associated with the task of a CCTV operator: visual search and cognitive/perceptual overload, attentional failures, vulnerability to distraction, and decision-making in a dynamically evolving environment. While not directly applied to surveillance issues, we suggest that the NSEEV (noticing – salience, effort, expectancy, value) model of attention could provide a useful theoretical basis for understanding the challenges faced in detection and monitoring tasks. Having identified cognitive limitations of the human operator, this review sets out a research agenda for further understanding the cognitive functioning related to surveillance, and highlights the need to consider the human element at the design stage when developing technological solutions to security surveillance

Measuring mental workload with EEG+fNIRS

We studied the capability of a Hybrid functional neuroimaging technique to quantify human mental workload (MWL). We have used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) as imaging modalities with 17 healthy subjects performing the letter n-back task, a standard experimental paradigm related to working memory (WM). The level of MWL was parametrically changed by variation of n from 0 to 3. Nineteen EEG channels were covering the whole-head and 19 fNIRS channels were located on the forehead to cover the most dominant brain region involved in WM. Grand block averaging of recorded signals revealed specific behaviors of oxygenated-hemoglobin level during changes in the level of MWL. A machine learning approach has been utilized for detection of the level of MWL. We extracted different features from EEG, fNIRS, and EEG+fNIRS signals as the biomarkers of MWL and fed them to a linear support vector machine (SVM) as train and test sets. These features were selected based on their sensitivity to the changes in the level of MWL according to the literature. We introduced a new category of features within fNIRS and EEG+fNIRS systems. In addition, the performance level of each feature category was systematically assessed. We also assessed the effect of number of features and window size in classification performance. SVM classifier used in order to discriminate between different combinations of cognitive states from binary- and multi-class states. In addition to the cross-validated performance level of the classifier other metrics such as sensitivity, specificity, and predictive values were calculated for a comprehensive assessment of the classification system. The Hybrid (EEG+fNIRS) system had an accuracy that was significantly higher than that of either EEG or fNIRS. Our results suggest that EEG+fNIRS features combined with a classifier are capable of robustly discriminating among various levels of MWL. Results suggest that EEG+fNIRS should be preferred to only EEG or fNIRS, in developing passive BCIs and other applications which need to monitor users' MWL

Social Cognition for Human-Robot Symbiosis—Challenges and Building Blocks

The next generation of robot companions or robot working partners will need to satisfy social requirements somehow similar to the famous laws of robotics envisaged by Isaac Asimov time ago (Asimov, 1942). The necessary technology has almost reached the required level, including sensors and actuators, but the cognitive organization is still in its infancy and is only partially supported by the current understanding of brain cognitive processes. The brain of symbiotic robots will certainly not be a “positronic” replica of the human brain: probably, the greatest part of it will be a set of interacting computational processes running in the cloud. In this article, we review the challenges that must be met in the design of a set of interacting computational processes as building blocks of a cognitive architecture that may give symbiotic capabilities to collaborative robots of the next decades: (1) an animated body-schema; (2) an imitation machinery; (3) a motor intentions machinery; (4) a set of physical interaction mechanisms; and (5) a shared memory system for incremental symbiotic development. We would like to stress that our approach is totally un-hierarchical: the five building blocks of the shared cognitive architecture are fully bi-directionally connected. For example, imitation and intentional processes require the “services” of the animated body schema which, on the other hand, can run its simulations if appropriately prompted by imitation and/or intention, with or without physical interaction. Successful experiences can leave a trace in the shared memory system and chunks of memory fragment may compete to participate to novel cooperative actions. And so on and so forth. At the heart of the system is lifelong training and learning but, different from the conventional learning paradigms in neural networks, where learning is somehow passively imposed by an external agent, in symbiotic robots there is an element of free choice of what is worth learning, driven by the interaction between the robot and the human partner. The proposed set of building blocks is certainly a rough approximation of what is needed by symbiotic robots but we believe it is a useful starting point for building a computational framework