A pBCI to Predict Attentional Error Before it Happens in Real Flight Conditions

Dehais, Frédéric; Rida, Imad; Roy, Raphaëlle N.; Iversen, John R.; Mullen, Tim; Callan, Daniel E.

doi:10.1109/smc.2019.8914010

Cited by 23 publications

(23 citation statements)

References 42 publications

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“…As the objective of our model was to represent the pilot's current state of knowledge about a given situation, individual differences were modeled as knowledge differences by withholding information depending on EEG data. More comprehensive approaches that, for example, integrate measures of pilots’ resource availability (e.g., Dehais, Rida, et al, 2019) might be able to anticipate missed alerts and to model pilots' differences in knowledge about a situation as a function of their architectural differences (Taatgen, 1999). Albeit being more complex, such modeling of architectural differences could provide diagnostic information and thereby explain why certain messages were not processed correctly in a complex task.…”

Section: Discussionmentioning

confidence: 99%

A Neuroadaptive Cognitive Model for Dealing With Uncertainty in Tracing Pilots' Cognitive State

Klaproth

Halbrügge

Krol³

et al. 2020

Topics in Cognitive Science

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A model‐based approach for cognitive assistance is proposed to keep track of pilots' changing demands in dynamic situations. Based on model‐tracing with flight deck interactions and EEG recordings, the model is able to represent individual pilots' behavior in response to flight deck alerts. As a first application of the concept, an ACT‐R cognitive model is created using data from an empirical flight simulator study on neurophysiological signals of missed acoustic alerts. Results show that uncertainty of individual behavior representation can be significantly reduced by combining cognitive modeling with EEG data. Implications for cognitive assistance in aviation are discussed.

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Section: Discussionmentioning

confidence: 99%

A Neuroadaptive Cognitive Model for Dealing With Uncertainty in Tracing Pilots' Cognitive State

Klaproth

Halbrügge

Krol³

et al. 2020

Topics in Cognitive Science

View full text Add to dashboard Cite

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“…Under nominal conditions, interaction between the dorsal and the ventral pathways ensure optimal trade-off between those attentional strategies associated with exploitation and exploration. However, under conditions of high task demand or stress or fatigue, this mechanism may become biased toward dominance of the dorsal over the ventral network, leading to attentional phenomena associated with inflexibility (Todd et al, 2005;Durantin et al, 2017;Edworthy et al, 2018;Dehais et al, 2019a). A similar dynamic of bias and dominance is apparent in the relationship between the dorsal and ventral pathways and the default mode network (Andrews-Hanna et al, 2014), which is associated with mindwandering, spontaneous thoughts and disengagement from taskrelated stimuli (Fox et al, 2015).…”

Section: Attentional Dynamics and Dominance Effectsmentioning

confidence: 99%

“…MPFC (Harrivel et al, 2013;Durantin et al, 2015) DLPFC (Harrivel et al, 2013) DLPFC (Durantin et al, 2014;Fairclough et al, 2019) Left PFC (Kalia et al, 2018) occipital lobe (Kojima and Suzuki, 2010) EEG α power over occipital sites (Gouraud et al, 2018) (α and (β power (auditory stimuli) (Braboszcz and Delorme, 2011) (θ power (auditory stimuli) (Braboszcz and Delorme, 2011) N1 (Kam et al, 2011) N4 (O'Connell et al, 2009) P1 (Kam et al, 2011) P2 (Braboszcz and Delorme, 2011) P3 (Schooler et al, 2011) frontal θ power (Gärtner et al, 2014) P3 (Dierolf et al, 2017) frontal (θ power and parietal (α power (Ewing et al, 2016;Fairclough and Ewing, 2017) Event Related Coherence between midfrontal and right-frontal electrodes (Carrillo-De-La-Pena and García-Larrea, 2007) (α band power (Mathewson et al, 2009) P1 (Pourtois et al, 2006;Mathewson et al, 2009) P2 (Mathewson et al, 2009) N170 (Pourtois et al, 2006) P3 (Pourtois et al, 2006;Mathewson et al, 2009) N1 (Callan et al, 2018;Dehais et al, 2019a,b) P3 (Puschmann et al, 2013;Scannella et al, 2013;Giraudet et al, 2015b;Dehais et al, 2019a,b) (α power in IFG (Dehais et al, 2...…”

Section: Adaptation Of the User Interfacementioning

confidence: 99%

A Neuroergonomics Approach to Mental Workload, Engagement and Human Performance

et al. 2020

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The assessment and prediction of cognitive performance is a key issue for any discipline concerned with human operators in the context of safety-critical behavior. Most of the research has focused on the measurement of mental workload but this construct remains difficult to operationalize despite decades of research on the topic. Recent advances in Neuroergonomics have expanded our understanding of neurocognitive processes across different operational domains. We provide a framework to disentangle those neural mechanisms that underpin the relationship between task demand, arousal, mental workload and human performance. This approach advocates targeting those specific mental states that precede a reduction of performance efficacy. A number of undesirable neurocognitive states (mind wandering, effort withdrawal, perseveration, inattentional phenomena) are identified and mapped within a twodimensional conceptual space encompassing task engagement and arousal. We argue that monitoring the prefrontal cortex and its deactivation can index a generic shift from a nominal operational state to an impaired one where performance is likely to degrade. Neurophysiological, physiological and behavioral markers that specifically account for these states are identified. We then propose a typology of neuroadaptive countermeasures to mitigate these undesirable mental states.

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“…On the other hand, humans can handle unexpected events, they have intuition, are creative and capable of making complex (e.g., ethical or moral) decisions. However, the human operator may experience high workload [6] and cognitive fatigue [7,8] that in turn can impair their attentional [9] and executive abilities [10]. In this cybernetic system context, several challenges have arisen such as the implementation of human-robots monitoring autonomously and the volunteers have to supervise the robot's status and manage the water tank supply level.…”

Section: Introductionmentioning

confidence: 99%

Towards Mixed-Initiative Human–Robot Interaction: Assessment of Discriminative Physiological and Behavioral Features for Performance Prediction

Chanel

Roy

Dehais

et al. 2020

Sensors

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The design of human–robot interactions is a key challenge to optimize operational performance. A promising approach is to consider mixed-initiative interactions in which the tasks and authority of each human and artificial agents are dynamically defined according to their current abilities. An important issue for the implementation of mixed-initiative systems is to monitor human performance to dynamically drive task allocation between human and artificial agents (i.e., robots). We, therefore, designed an experimental scenario involving missions whereby participants had to cooperate with a robot to fight fires while facing hazards. Two levels of robot automation (manual vs. autonomous) were randomly manipulated to assess their impact on the participants’ performance across missions. Cardiac activity, eye-tracking, and participants’ actions on the user interface were collected. The participants performed differently to an extent that we could identify high and low score mission groups that also exhibited different behavioral, cardiac and ocular patterns. More specifically, our findings indicated that the higher level of automation could be beneficial to low-scoring participants but detrimental to high-scoring ones, and vice versa. In addition, inter-subject single-trial classification results showed that the studied behavioral and physiological features were relevant to predict mission performance. The highest average balanced accuracy (74%) was reached using the features extracted from all input devices. These results suggest that an adaptive HRI driving system, that would aim at maximizing performance, would be capable of analyzing such physiological and behavior markers online to further change the level of automation when it is relevant for the mission purpose.

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A pBCI to Predict Attentional Error Before it Happens in Real Flight Conditions

Cited by 23 publications

References 42 publications

A Neuroadaptive Cognitive Model for Dealing With Uncertainty in Tracing Pilots' Cognitive State

A Neuroadaptive Cognitive Model for Dealing With Uncertainty in Tracing Pilots' Cognitive State

A Neuroergonomics Approach to Mental Workload, Engagement and Human Performance

Towards Mixed-Initiative Human–Robot Interaction: Assessment of Discriminative Physiological and Behavioral Features for Performance Prediction

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