A passive brain–computer interface application for the mental workload assessment on professional air traffic controllers during realistic air traffic control tasks

Aricò, Pietro; Borghini, Gianluca; Flumeri, Gianluca Di; Colosimo, Alfredo; Pozzi, Simone; Babiloni, Fabio

doi:10.1016/bs.pbr.2016.04.021

Cited by 113 publications

(115 citation statements)

References 67 publications

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“…The EEG frequency bands [frequency resolution of 0.5 (Hz)] of interest have been defined for each ATCO by the estimation of the Individual Alpha Frequency (IAF) value (Klimesch, 1999; Babiloni et al, 2000). At this point, the classification algorithm automatic stop Stepwise Linear Discriminant Analysis (asSWLDA, patent number P1108IT00, Aricò et al, 2015a, 2016) has been used to identify the most relevant discriminant features among the different experimental conditions (i.e., Easy 0 and Hard 0 ), related to the lowest and the highest task complexity. Once identified, the asSWLDA classifier assigns to each significant feature specific weights ( w i train ), plus a bias ( b train ).…”

Section: Methodsmentioning

confidence: 99%

“…Briefly, a BCI is defined as “ a system that measures Central Nervous System (CNS) activity and converts it into artificial output that replaces, restores, enhances or improves natural CNS output and thereby changes the ongoing interactions between the CNS and its external or internal environment” (Wolpaw and Wolpaw, 2012). Such definition summarizes the progresses of the scientific community in this field during the last decades, since at the moment the possibility of using the BCI systems outside the laboratories (Aloise et al, 2010; Blankertz et al, 2010; Aricò et al, 2011; Riccio et al, 2015; Schettini et al, 2015), by developing applications in everyday life is not just a theory but something very close to real applications (Zander et al, 2009; Blankertz et al, 2010; Aricò et al, 2016). This technology has been defined passive Brain-Computer Interface (pBCI).…”

Section: Introductionmentioning

confidence: 99%

“…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). Among all these techniques, Aricò et al (2016) have highlighted the clear advantages of using EEG signal to implement pBCI applications. Several studies, in particular in the aviation domain, have developed efficient EEG-based mental workload indexes.…”

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.

184

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: Methodsmentioning

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.

184

143

View full text Add to dashboard Cite

show abstract

“…The increase in the frontal theta in parallel of the mental effort increase has been specifically identified as peculiar of the mental effort, since the physical effort produces instead an increase in alpha and beta one power (Smit et al, 2005). Furthermore, it has been seen frontal theta to rise in correspondence to the task complexity (Gevins et al, 1998; Aricò et al, 2016; Toppi et al, 2016; Vecchiato et al, 2016). Additionally, Gevins and colleagues provided evidences of frontal theta activity increase in correspondence of high task load in an n -back task, further supporting the relation between this index and the mental workload concept (Gevins et al, 1997, 1998; Smith and Gevins, 2005a,b).…”

Section: Introductionmentioning

confidence: 99%

Neurophysiological Measures of the Perception of Antismoking Public Service Announcements Among Young Population

Cartocci

Modica

Rossi

et al. 2018

Front. Hum. Neurosci.

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Tobacco constitutes a global emergency with totally preventable millions of deaths per year and smoking-related illnesses. Public service announcements (PSAs) are the main tool against smoking and by now their efficacy is still assessed through questionnaires and metrics, only months after their circulation. The present study focused on the young population, because at higher risk of developing tobacco addiction, investigating the reaction to the vision of Effective, Ineffective and Awarded antismoking PSAs through: electroencephalography (EEG), autonomic activity variation (Galvanic skin response—GSR- and Heart Rate—HR-) and Eye-Tracking (ET). The employed indices were: the EEG frontal alpha band asymmetry and the frontal theta; the Emotional Index (EI), deriving from the GSR and HR signals matching; the ET Visual Attention (VA) index, based on the ratio between the total time spent fixating an area of interest (AOI) and its area. Smokers expressed higher frontal alpha asymmetry values in comparison to non-smokers. Concerning frontal theta, Awarded PSAs reported the highest values in comparison to both Effective and Ineffective PSAs. EI results highlighted that lowest values were expressed by Heavy Smokers (HS), and Effective PSAs obtained the highest EI values. Finally, concerning the Effective PSAs, regression analysis highlighted a correlation between the number of cigarettes smoked by participants (independent variable) and frontal alpha asymmetry, frontal theta and EI values. ET results suggested that for the Ineffective PSAs the main focus were texts, while for the Effective and Awarded PSAs were the visual elements. Results support the use of methods aimed at assessing the physiological reaction for the evaluation of PSAs images, in particular when considering the smoking habits of target populations.

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“…The EEG frequency bands [frequency resolution of 0.5(Hz)] of interest will be defined for each ATCo by the estimation of the Individual Alpha Frequency value, as stated previously. At this point, the classification algorithm automatic stop Stepwise Linear Discriminant Analysis (asSWLDA, [67]) will be used to identify the most relevant discriminant features among the two different experimental conditions related to the lowest and the highest level of vigilance. Once identified, the asSWLDA classifier will assigns to each significant feature specific weights plus a bias.…”

Section: ─ Function 4: Pattern Classification Algorithmsmentioning

confidence: 99%

MINIMA Project: Detecting and Mitigating the Negative Impact of Automation

Berberian

Ohneiser

Crescenzio

et al. 2017

Engineering Psychology and Cognitive Ergonomics: Performance, Emotion and Situation Awareness

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A passive brain–computer interface application for the mental workload assessment on professional air traffic controllers during realistic air traffic control tasks

Cited by 113 publications

References 67 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

Neurophysiological Measures of the Perception of Antismoking Public Service Announcements Among Young Population

MINIMA Project: Detecting and Mitigating the Negative Impact of Automation

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