EEG-Based Human Factors Evaluation of Conflict Resolution Aid and Tactile User Interface in Future Air Traffic Control Systems

Hou, Xiyuan; Trapsilawati, Fitri; Liu, Yisi; Sourina, Olga; Chen, Chun‐Hsien; Müeller-Wittig, Wolfgang; Ang, Wei Tech

doi:10.1007/978-3-319-41682-3_73

Cited by 18 publications

(11 citation statements)

References 18 publications

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“…There was also a coherent correlation in the frontal and parietal lobes between pilot and copilot during collaborative tasks. Hou et al (2017) also validated use of the EEG to measure real-time workload in the ATC domain.…”

Section: Literature Reviewmentioning

confidence: 99%

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EEG-Based Analysis of Air Traffic Conflict: Investigating Controllers’ Situation Awareness, Stress Level and Brain Activity during Conflict Resolution

et al. 2019

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The effects of air traffic conflict geometry have been well investigated in prior studies, particularly in the context of the pilot, though little in the context of the air traffic control officer (ATCO). No study to date has investigated the effects of conflict geometry on human factors variables of ATCOs through objective and physiological approaches. This study examines the effects of conflict geometries on ATCO situation awareness, stress level and brain activity during conflict resolution. Fifteen participants were instructed to resolve six different conflict geometries: crossing level, crossing non-level, converging level, converging non-level, overtaking level, and overtaking non-level. The results indicate that converging and crossing conflicts led to lower situation awareness (SA), higher stress level, and higher theta activation at the temporal and parietal lobes. Level conflict led to lower SA. The findings offer two implications, providing insights for the formal guidelines in ATC conflict resolution training and provision of inputs for the conflict resolution aid development.

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Section: Literature Reviewmentioning

confidence: 99%

“…Further, there are several measures of stress level, including subjective, performance-based and physiological stress (Hou et al, 2017). Subjective stress measurement is typically performed using questionnaires, but the results tend to also be subjective and are hard to continuously evaluate across the entire experiment or observation.…”

Section: Introductionmentioning

confidence: 99%

EEG-Based Analysis of Air Traffic Conflict: Investigating Controllers’ Situation Awareness, Stress Level and Brain Activity during Conflict Resolution

et al. 2019

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“…As discussed in the drowsy driver example, monitoring real-time mental workload and vigilance is of particular importance in safety-critical environments (Lin C. T. et al, 2010 ; Khan and Hong, 2015 ; Aricò et al, 2017 ). Non-invasive BCIs that detect drops in attention level and increased mental fatigue can be utilized in a broad range of operational environments and application domains including aviation (Aricò et al, 2016 ; Hou et al, 2017 ) and industrial workspaces (Schultze-Kraft et al, 2012 ) where safety and efficiency are important, as well as educational and healthcare setups where the system can provide feedback from learners to a teacher (Ko et al, 2017 ; Spüler et al, 2017 ), evaluate sustained attention in e-learning platforms (Chen et al, 2017 ), and execute attention training for clinical patients who suffer from attention deficit hyperactivity disorder (ADHD) (Lim et al, 2019 ). It is even suggested that detection of attention level can be employed in a hybrid BCI system in which an attention classifier is integrated with other BCI algorithms in order to confirm users' focus on the BCI task and validate the produced response, thereby yielding a more reliable and robust performance (Diez et al, 2015 ).…”

Section: Bcis and Cognitive/affective State Estimationmentioning

confidence: 99%

Passive Brain-Computer Interfaces for Enhanced Human-Robot Interaction

Alimardani

Hiraki

2020

Front. Robot. AI

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Brain-computer interfaces (BCIs) have long been seen as control interfaces that translate changes in brain activity, produced either by means of a volitional modulation or in response to an external stimulation. However, recent trends in the BCI and neurofeedback research highlight passive monitoring of a user's brain activity in order to estimate cognitive load, attention level, perceived errors and emotions. Extraction of such higher order information from brain signals is seen as a gateway for facilitation of interaction between humans and intelligent systems. Particularly in the field of robotics, passive BCIs provide a promising channel for prediction of user's cognitive and affective state for development of a user-adaptive interaction. In this paper, we first illustrate the state of the art in passive BCI technology and then provide examples of BCI employment in human-robot interaction (HRI). We finally discuss the prospects and challenges in integration of passive BCIs in socially demanding HRI settings. This work intends to inform HRI community of the opportunities offered by passive BCI systems for enhancement of human-robot interaction while recognizing potential pitfalls.

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“…Another advantage of using synthetic microworld is that it allows recruiting participants that are not necessarily qualified professionals. Several studies employing synthetic ATC simulations involved novices in the ATC domain or students with some ATC knowledge (Hou et al, 2017;Salden et al, 2006). For example, in the current experiment, a fully realistic simulation would have required numerous qualified controllers with a specific training for the Roissy-Charles-de-Gaulle airport.…”

Section: Atc Taskmentioning

confidence: 99%

Encoding decisions and expertise in the operator's eyes: Using eye-tracking as input for system adaptation

Causse

Lancelot

Jordan

et al. 2019

International Journal of Human-Computer Studies

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A B S T R A C TWe investigated the possibility of developing a decision support system (DSS) that integrates eye-fixation measurements to better adapt its suggestions. Indeed, eye fixation give insight into human decision-making: Individuals tend to pay more attention to key information in line with their upcoming selection. Thus, eyefixation measures can help the DSS to better capture the context that determines user decisions. Twenty-two participants performed a simplified Air Traffic Control (ATC) simulation in which they had to decide to accept or to modify route suggestions according to specific parameter values displayed on the screen. Decisions and fixation times on each parameter were recorded. The user fixation times were used by an algorithm to estimate the utility of each parameter for its decision. Immediately after this training phase, the algorithm generated new route suggestions under two conditions: 1) Taking into account the participant's decisions, 2) Taking into account the participant's decisions plus their visual behavior using the measurements of dwell times on displayed parameters. Results showed that system suggestions were more accurate than the base system when taking into account the participant's decisions, and even more accurate using their dwell times. Capturing the crucial information for the decision using the eye tracker accelerated the DSS learning phase, and thus helped to further enhance the accuracy of consecutive suggestions. Moreover, exploratory eye-tracking analysis reflected two different stages of the decision-making process, with longer dwell times on relevant parameters (i.e. involved in a rule) during the entire decision time course, and frequency of fixations on these relevant parameters that increased, especially during the last fixations prior to the decision. Consequently, future DSS integrating eyetracking data should pay specific care to the final fixations prior to the decision. In general, our results emphasize the potential interest of eye-tracking to enhance and accelerate system adaptation to user preference, knowledge, and expertise.

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EEG-Based Human Factors Evaluation of Conflict Resolution Aid and Tactile User Interface in Future Air Traffic Control Systems

Cited by 18 publications

References 18 publications

EEG-Based Analysis of Air Traffic Conflict: Investigating Controllers’ Situation Awareness, Stress Level and Brain Activity during Conflict Resolution

EEG-Based Analysis of Air Traffic Conflict: Investigating Controllers’ Situation Awareness, Stress Level and Brain Activity during Conflict Resolution

Passive Brain-Computer Interfaces for Enhanced Human-Robot Interaction

Encoding decisions and expertise in the operator's eyes: Using eye-tracking as input for system adaptation

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