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Cited by 168Human-Automation Interaction and SafetyEEG and Brain-Computer InterfacesTraffic and Road Safety

EEG-Based Mental Workload Neurometric to Evaluate the Impact of Different Traffic and Road Conditions in Real Driving Settings

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Di Flumeri, Gianluca, Borghini, Gianluca, Aricò, Pietro, Sciaraffa, Nicolina, Lanzi, Paola, Pozzi, Simone, Vignali, Valeria, Lantieri, Claudio +3 more·2018·Frontiers in Human Neuroscience
DOI 10.3389/fnhum.2018.00509PMC PMC6305466
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Abstract

Car driving is considered a very complex activity, consisting of different concomitant tasks and subtasks, thus it is crucial to understand the impact of different factors, such as road complexity, traffic, dashboard devices, and external events on the driver's behavior and performance. For this reason, in particular situations the cognitive demand experienced by the driver could be very high, inducing an excessive experienced mental workload and consequently an increasing of error commission probability. In this regard, it has been demonstrated that human error is the main cause of the 57% of road accidents and a contributing factor in most of them. In this study, 20 young subjects have been involved in a real driving experiment, performed under different traffic conditions (rush hour and not) and along different road types (main and secondary streets). Moreover, during the driving tasks different specific events, in particular a pedestrian crossing the road and a car entering the traffic flow just ahead of the experimental subject, have been acted. A Workload Index based on the Electroencephalographic (EEG), i.e., brain activity, of the drivers has been employed to investigate the impact of the different factors on the driver's workload. Eye-Tracking (ET) technology and subjective measures have also been employed in order to have a comprehensive overview of the driver's perceived workload and to investigate the different insights obtainable from the employed methodologies. The employment of such EEG-based Workload index confirmed the significant impact of both traffic and road types on the drivers' behavior (increasing their workload), with the advantage of being under real settings. Also, it allowed to highlight the increased workload related to external events while driving, in particular with a significant effect during those situations when the traffic was low. Finally, the comparison between methodologies revealed the higher sensitivity of neurophysiological measures with respect to ET and subjective ones. In conclusion, such an EEG-based Workload index would allow to assess objectively the mental workload experienced by the driver, standing out as a powerful tool for research aimed to investigate drivers' behavior and providing additional and complementary insights with respect to traditional methodologies employed within road safety research.

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  • EEG features in the time domain (P300 ERPs) and frequency domain (EEG frequency bands) are modulated by difficulty of task variations.

    “Through EEG measures, we extracted features (i) in the time domain (i.e., ERPs analysis, in particular P300 ERPs) and (ii) in the frequency domain (i.e., EEG frequency bands), both modulated the difficulty of the task variations32–34.”

    From The impact of multisensory integration and perceptual load in virtual reality settings on performance, workload and presence (2021) · cites this paper as [33]

    Materials and Methods (The Data Collection; page not provided) describes theta and alpha frequency-band features, and Results (Experimental Design Validation; page not provided) reports that an EEG-band indicator increased significantly on the harder road segment. The cited paper does not report P300 ERP or other time-domain features being modulated by task difficulty. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Authors
Di Flumeri, G., Borghini, G., Aricò, P., Sciaraffa, N., Lanzi, P., Pozzi, S., Vignali, V., Lantieri, C., Bichicchi, A., Simone, A., & Babiloni, F.
Affiliations
  • Fondazione Santa Lucia (IT)
  • Deep Blue (Italy) (IT)
  • University of Bologna (IT)
  • Hangzhou Dianzi University (CN)
ORCID
Venue
Frontiers in Human Neuroscience
Publisher
Frontiers Media SA
Published
2018-12-18
Volume
12
Pages
509-509
ISSN
1662-5161 (ISSN-L)
DOI
10.3389/fnhum.2018.00509
PMC
PMC6305466
OpenAlex
W2906324501
Cited by
168
References
1
Citations by year
2026: 15 · 2025: 21 · 2024: 23 · 2023: 21 · 2022: 26 · 2021: 25 · 2020: 22 · 2019: 15
FWCI
20.99
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Human Neuroscience (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • IRIS Research product catalog (Sapienza University of Rome) (submittedVersion, cc-by)
  • Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • European Commission (723386, H2020)
  • Università di Bologna
  • Horizon 2020 Framework Programme (723386)
Topics
  • Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
Keywords
mental workload, traffic conditions, peak hours, road type, real driving conditions, driver behavior, eye tracking, perceived workload, road safety, human error, neurophysiological measures

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