On the Use of Cognitive Neurometric Indexes in Aeronautic and Air Traffic Management Environments

Flumeri, Gianluca Di; Borghini, Gianluca; Aricò, Pietro; Colosimo, Alfredo; Pozzi, Simone; Bonelli, Stefano; Golfetti, Alessia; Kong, Wanzeng; Babiloni, Fabio

doi:10.1007/978-3-319-24917-9_5

Cited by 34 publications

(26 citation statements)

References 13 publications

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“…More recently, Shou et al (2012) found that “the frontal theta EEG activity was a sensitive and reliable metric to assess workload […] during an ATC task at the resolution of minute (s).” The same findings have been highlighted by Borghini et al (2013) involving pilots in flight simulation tasks. In other recent studies involving ATCOs (Aricò et al, 2013, 2014, 2015b,c; Borghini et al, 2014; Di Flumeri et al, 2015; Toppi et al, 2016), it was demonstrated how it was possible to compute an EEG-based Workload Index able to significantly discriminate the workload demands of the ATM task, and to monitor them continuously by using frontal-parietal brain features. Other studies about the mental workload estimation by using neurophysiological indexes, have been proposed also in other operational contexts (Car drivers - Kohlmorgen et al, 2007; Borghini et al, 2012a; military domain - Dorneich et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

“…Secondly, compared with performance measures , the neurophysiological ones may be recorded continuously without using overt responses (i.e., additional tasks) and may provide a direct measure of the mental (covert) activities of the operator. Also, neurophysiological measures have higher resolution than performance measures (Di Flumeri et al, 2015). Finally, neurophysiological measures can be used not only to trigger the AA, but also to highlight why AAs are important for enhance the safety in high-risk and high-demanding tasks.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is crucial to have a reliable estimation of the actual mental workload experienced by the operator along the execution of the task, in order to make the user interface able to preserve a proper level of the user's mental workload, avoiding under- or overload state (Hancock and Warm, 1989; Borghini et al, 2012, 2015b). In this regard, neurophysiological techniques have been demonstrated to be able to assess mental workload of humans with a high reliability, even in operational environments (Mühl et al, 2014; Borghini et al, 2015a; Di Flumeri et al, 2015). Many neurophysiological measures have been used for the mental workload assessment, including Electroencephalography (EEG), functional Near-InfraRed (fNIR) imaging, functional Magnetic Resonance Imaging (fMRI), and other biosignals, such as Electrocardiography (ECG) and Galvanic Skin Response (GSR) (Wood and Grafman, 2003; Ramnani and Owen, 2004; Borghini et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Automation Triggered by EEG-Based Mental Workload Index: A Passive Brain-Computer Interface Application in Realistic Air Traffic Control Environment

Aricò¹,

Borghini²,

Flumeri³

et al. 2016

Front. Hum. Neurosci.

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190

143

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Adaptive Automation (AA) is a promising approach to keep the task workload demand within appropriate levels in order to avoid both the under- and over-load conditions, hence enhancing the overall performance and safety of the human-machine system. The main issue on the use of AA is how to trigger the AA solutions without affecting the operative task. In this regard, passive Brain-Computer Interface (pBCI) systems are a good candidate to activate automation, since they are able to gather information about the covert behavior (e.g., mental workload) of a subject by analyzing its neurophysiological signals (i.e., brain activity), and without interfering with the ongoing operational activity. We proposed a pBCI system able to trigger AA solutions integrated in a realistic Air Traffic Management (ATM) research simulator developed and hosted at ENAC (École Nationale de l'Aviation Civile of Toulouse, France). Twelve Air Traffic Controller (ATCO) students have been involved in the experiment and they have been asked to perform ATM scenarios with and without the support of the AA solutions. Results demonstrated the effectiveness of the proposed pBCI system, since it enabled the AA mostly during the high-demanding conditions (i.e., overload situations) inducing a reduction of the mental workload under which the ATCOs were operating. On the contrary, as desired, the AA was not activated when workload level was under the threshold, to prevent too low demanding conditions that could bring the operator's workload level toward potentially dangerous conditions of underload.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Automation Triggered by EEG-Based Mental Workload Index: A Passive Brain-Computer Interface Application in Realistic Air Traffic Control Environment

Aricò¹,

Borghini²,

Flumeri³

et al. 2016

Front. Hum. Neurosci.

Self Cite

190

143

View full text Add to dashboard Cite

show abstract

“…Another area of application is in training procedures, in which displaying the current workload level to the trainer as well as to the trainee her/himself is expected to facilitate learning or to make the alignment of difficulty levels to the trainee's progress more efficient (Borghini et al, 2016). This possibility has been explored for pilots (Borghini et al, 2013), air traffic controllers (Di Flumeri et al, 2015), and shipmasters (Miklody et al, 2016). The technique could similarly be used to improve infrastructure, by testing, for example, which features of streets, harbors, and the like require maneuvers that are likely to induce high workload.…”

Section: Advanced Monitoring Of Workloadmentioning

confidence: 99%

The Berlin Brain-Computer Interface: Progress Beyond Communication and Control

et al. 2016

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The combined effect of fundamental results about neurocognitive processes and advancements in decoding mental states from ongoing brain signals has brought forth a whole range of potential neurotechnological applications. In this article, we review our developments in this area and put them into perspective. These examples cover a wide range of maturity levels with respect to their applicability. While we assume we are still a long way away from integrating Brain-Computer Interface (BCI) technology in general interaction with computers, or from implementing neurotechnological measures in safety-critical workplaces, results have already now been obtained involving a BCI as research tool. In this article, we discuss the reasons why, in some of the prospective application domains, considerable effort is still required to make the systems ready to deal with the full complexity of the real world.

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“…This type of method is reliable because the physiological data can directly reflect a driver's physiological state and will change when the driver's attention changes, and the process of signal acquisition is more convenient and easier with the development of wireless signal acquisition technology. Researchers in this field are paying more attention to the application of physiological signals, such as electrocardiogram (ECG) [13][14][15], electroencephalogram (EEG) [16][17][18][19][20][21], galvanic skin response (GSR) [22][23][24][25], and electrooculogram (EOG) [26][27][28]. Improving the accuracy and reliability of recognition is a research goal of scholars.…”

Section: Introductionmentioning

confidence: 99%

A Novel Classification Method for a Driver’s Cognitive Stress Level by Transferring Interbeat Intervals of the ECG Signal to Pictures

Huang¹,

Luo²,

Peng³

2020

Sensors

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In this study, a novel classification method for a driver's cognitive stress level was proposed, whereby the interbeat intervals extracted from an electrocardiogram (ECG) signal were transferred to pictures, and a convolution neural network (CNN) was used to train the pictures to classify a driver's cognitive stress level. First, we defined three levels of tasks and collected the ECG signal of the driver at different cognitive stress levels by designing and performing a driving simulation experiment. We extracted the interbeat intervals and converted them to pictures according to the number of consecutive interbeat intervals in each picture. Second, the CNN model was used to train the data set to recognize the cognitive stress levels. Classification accuracies of 100%, 91.6% and 92.8% were obtained for the training set, validation set and test set, respectively, and were compared with those the BP neural network. Last, we discussed the influence of the number of interbeat intervals in each picture on the performance of the proposed classification method. The results showed that the performance initially improved with an increase in the number of interbeat intervals. A downward trend was observed when the number exceeded 40, and when the number was 40, the model performed best with the highest accuracy (98.79%) and a relatively low relative standard deviation (0.019).

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On the Use of Cognitive Neurometric Indexes in Aeronautic and Air Traffic Management Environments

Cited by 34 publications

References 13 publications

Adaptive Automation Triggered by EEG-Based Mental Workload Index: A Passive Brain-Computer Interface Application in Realistic Air Traffic Control Environment

Adaptive Automation Triggered by EEG-Based Mental Workload Index: A Passive Brain-Computer Interface Application in Realistic Air Traffic Control Environment

The Berlin Brain-Computer Interface: Progress Beyond Communication and Control

A Novel Classification Method for a Driver’s Cognitive Stress Level by Transferring Interbeat Intervals of the ECG Signal to Pictures

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