EEG-based mental workload neurometric to evaluate the impact of different traffic and road conditions in real driving settings

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

A LightGBM-Based EEG Analysis Method for Driver Mental States Classification

Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated. However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is based on gradient boosting framework for EEG mental states identification. ,e comparable results with traditional classifiers, such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI)

EEG-based cognitive control behaviour assessment: an ecological study with professional air traffic controllers

Several models defining different types of cognitive human behaviour are available. For this work, we have selected the Skill, Rule and Knowledge (SRK) model proposed by Rasmussen in 1983. This model is currently broadly used in safety critical domains, such as the aviation. Nowadays, there are no tools able to assess at which level of cognitive control the operator is dealing with the considered task, that is if he/she is performing the task as an automated routine (skill level), as procedures-based activity (rule level), or as a problem-solving process (knowledge level). Several studies tried to model the SRK behaviours from a Human Factor perspective. Despite such studies, there are no evidences in which such behaviours have been evaluated from a neurophysiological point of view, for example, by considering brain activity variations across the different SRK levels. Therefore, the proposed study aimed to investigate the use of neurophysiological signals to assess the cognitive control behaviours accordingly to the SRK taxonomy. The results of the study, performed on 37 professional Air Traffic Controllers, demonstrated that specific brain features could characterize and discriminate the different SRK levels, therefore enabling an objective assessment of the degree of cognitive control behaviours in realistic setting

بررسی تغییرات ریتم آلفا به منظور ردیابی خستگی ذهنی راننده در روی شبیه ساز رانندگی

مقدمه: خستگی ذهنی یکی از علل اصلی حوادث جاده­ای است. بیش از 30 درصد حوادث به علت خواب­آلودگی و خستگی راننده اتفاق می­افتد، لذا شناسایی ابزارها و روش­هایی به منظور تشخیص زود هنگام خستگی و خواب ­آلودگی از اهمیت بسیاری در پیشگیری از حوادث برخورداراست. در این میان استفاده از روش­های بیولوژیکی مانند EEG می­تواند از معتبرترین روش­ها باشد. مواد و روش­ها: مطالعه حاضر به روش توصیفی-تحلیلی در روی 19 نفر از رانندگان سواری مرد انجام­گردید. به منظور القای بیشتر خستگی از رانندگان خواسته­شد که حداقل 18 ساعت قبل از آزمایش نخوابند و 12 ساعت پیش از آن از خوردن نوشیدنی­های کافیین­دار و مواد محرک خودداری نمایند. وضعیت خواب رانندگان از طریق فرم یادداشت خواب از یک هفته قبل کنترل می­شد. رانندگان می­بایست یک جاده 110 کیلومتری را با سرعت 90 کیلومتر در ساعت با حفظ مسیر حرکت طی­کنند. میزان خستگی ذهنی در هر 10 دقیقه با مقیاس خواب­آلودگی کرولینسکا ثبت می­شد. همچنین ارزشیابی ویدیویی از چهره راننده از لحاظ خستگی در هر 10 دقیقه توسط دو نفر از پژوهشگران آموزش­دیده انجام می­شد. در طول رانندگی روی شبیه­ساز، امواج مغزی با 16 کانال ثبت می­شد. پس از فیلترکردن و حذف سیگنال­های مزاحم، توان نسبی و مطلق آلفا در کانال­های مختلف محاسبه گردید. سپس از آمار توصیفی و ضریب همبستگی اسپیرمن و آزمون تی زوجی برای آزمون همبستگی و مقایسه میانگین­ها در 10 دقیقه ابتدایی و انتهایی رانندگی استفاده شد. یافته ­ها: این مطالعه نشان­داد که بین میزان خودارزیابی خستگی در 10 دقیقه ابتدایی و انتهایی مسیر اختلاف معنادار وجود داشت(001/0>P). این امر در مورد ارزشیابی ویدیویی نیز صدق می­کرد. میانگین توان مطلق آلفا در 10 دقیقه انتهایی نسبت به 10 دقیقه ابتدایی مسیر افزایش معنادار داشت(001/0>P) ، در حالی­که توان نسبی آلفا در 10 دقیقه انتهایی نسبت به 10 دقیقه ابتدایی مسیر تفاوتی نداشت. نتیجه­ گیری: خستگی ذهنی راننده یکی از مشکلات بسیار مهم رانندگان از دیدگاه ایمنی جاده به حساب می­آید. این مطالعه حاکی­است که امواج مغزی و بویژه توان مطلق آلفا می­تواند شاخص خوبی برای پیش­بینی زودهنگام خستگی ذهنی راننده باشد. Normal 0 false false false EN-US X-NONE F

Antismoking campaigns’ perception and gender differences: a comparison among EEG Indices

Human factors’ aim is to understand and evaluate the interactions between people and tasks, technologies, and environment. Among human factors, it is possible then to include the subjective reaction to external stimuli, due to individual’s characteristics and states of mind. These processes are also involved in the perception of antismoking public service announcements (PSAs), the main tool for governments to contrast the first cause of preventable deaths in the world: tobacco addiction. In the light of that, in the present article, it has been investigated through the comparison of different electroencephalographic (EEG) indices a typical item known to be able of influencing PSA perception, that is gender. In order to investigate the neurophysiological underpinnings of such different perception, we tested two PSAs: one with a female character and one with a male character. Furthermore, the experimental sample was divided into men and women, as well as smokers and nonsmokers. The employed EEG indices were the mental engagement (ME: the ratio between beta activity and the sum of alpha and theta activity); the approach/withdrawal (AW: the frontal alpha asymmetry in the alpha band); and the frontal theta activity and the spectral asymmetry index (SASI: the ratio between beta minus theta and beta plus theta). Results suggested that the ME and the AW presented an opposite trend, with smokers showing higher ME and lower AW than nonsmokers. The ME and the frontal theta also evidenced a statistically significant interaction between the kind of the PSA and the gender of the observers; specifically, women showed higher ME and frontal theta activity for the male character PSA. This study then supports the usefulness of the ME and frontal theta for purposes of PSAs targeting on the basis of gender issues and of the ME and the AW and for purposes of PSAs targeting on the basis of smoking habits

Neurophysiological Profile of Antismoking Campaigns

Over the past few decades, antismoking public service announcements (PSAs) have been used by governments to promote healthy behaviours in citizens, for instance, against drinking before the drive and against smoke. Effectiveness of such PSAs has been suggested especially for young persons. By now, PSAs efficacy is still mainly assessed through traditional methods (questionnaires and metrics) and could be performed only after the PSAs broadcasting, leading to waste of economic resources and time in the case of Ineffective PSAs. One possible countermeasure to such ineffective use of PSAs could be promoted by the evaluation of the cerebral reaction to the PSA of particular segments of population (e.g., old, young, and heavy smokers). In addition, it is crucial to gather such cerebral activity in front of PSAs that have been assessed to be effective against smoke (Effective PSAs), comparing results to the cerebral reactions to PSAs that have been certified to be not effective (Ineffective PSAs). &e eventual differences between the cerebral responses toward the two PSA groups will provide crucial information about the possible outcome of new PSAs before to its broadcasting. &is study focused on adult population, by investigating the cerebral reaction to the vision of different PSA images, which have already been shown to be Effective and Ineffective for the promotion of an antismoking behaviour. Results showed how variables as gender and smoking habits can influence the perception of PSA images, and how different communication styles of the antismoking campaigns could facilitate the comprehension of PSA’s message and then enhance the related impac

Probing ECG-based mental state monitoring on short time segments

Electrocardiography is used to provide features for mental state monitoring systems. There is a need for quick mental state assessment in some applications such as attentive user interfaces. We analyzed how heart rate and heart rate variability features are influenced by working memory load (WKL) and time-on-task (TOT) on very short time segments (5s) with both statistical significance and classification performance results. It is shown that classification of such mental states can be performed on very short time segments and that heart rate is more predictive of TOT level than heart rate variability. However, both features are efficient for WKL level classification. What's more, interesting interaction effects are uncovered: TOT influences WKL level classification either favorably when based on HR, or adversely when based on HRV. Implications for mental state monitoring are discussed

Eye blink characterization from frontal EEG electrodes using source separation and pattern recognition algorithms

Due to its major safety applications, including safe driving, mental fatigue estimation is a rapidly growing research topic in the engineering field. Most current mental fatigue monitoring systems analyze brain activity through electroencephalography (EEG). Yet eye blink analysis can also be added to help characterize fatigue states. It usually requires the use of additional devices, such as EOG electrodes, uncomfortable to wear, or more expensive eye trackers. However, in this article, a method is proposed to evaluate eye blink parameters using frontal EEG electrodes only. EEG signals, which are generally corrupted by ocular artifacts, are decomposed into sources by means of a source separation algorithm. Sources are then automatically classified into ocular or non-ocular sources using temporal, spatial and frequency features. The selected ocular source is back propagated in the signal space and used to localize blinks by means of an adaptive threshold, and then to characterize detected blinks. The method, validated on 11 different subjects, does not require any prior tuning when applied to a new subject, which makes it subject-independent. The vertical EOG signal was recorded during an experiment lasting 90 min in which the participants’ mental fatigue increased. The blinks extracted from this signal were compared to those extracted using frontal EEG electrodes. Very good performances were obtained with a true detection rate of 89% and a false alarm rate of 3%. The correlation between the blink parameters extracted from both recording modalities was 0.81 in average

EEG Theta Power Activity Reflects Workload among Army Combat Drivers: An Experimental Study

We thank Héctor Rieiro, Eduardo Bailon, and Jose M. Morales, (University of Granada) for their help in data processing. We also thank Lieutenant Colonel Francisco de Asís Vázquez Prieto (Training and Doctrine Command, Spanish Army) for his help in organizing the study.We aimed to evaluate the effects of mental workload variations, as a function of the road environment, on the brain activity of army drivers performing combat and non-combat scenarios in a light multirole vehicle dynamic simulator. Forty-one non-commissioned officers completed three standardized driving exercises with different terrain complexities (low, medium, and high) while we recorded their electroencephalographic (EEG) activity. We focused on variations in the theta EEG power spectrum, a well-known index of mental workload. We also assessed performance and subjective ratings of task load. The theta EEG power spectrum in the frontal, temporal, and occipital areas were higher during the most complex scenarios. Performance (number of engine stops) and subjective data supported these findings. Our findings strengthen previous results found in civilians on the relationship between driver mental workload and the theta EEG power spectrum. This suggests that EEG activity can give relevant insight into mental workload variations in an objective, unbiased fashion, even during real training and/or operations. The continuous monitoring of the warfighter not only allows instantaneous detection of over/underload but also might provide online feedback to the system (either automated equipment or the crew) to take countermeasures and prevent fatal errors.This work was supported by Santander Bank–CEMIX UGR-MADOC (grant number PINs2018-15 to CDP & LLDS) and the Centro Universitario de la Defensa–Zaragoza (grant numbers 2015-05 and 2017-03 to MVS). Additional support was obtained from the Unit of Excellence on Brain, Behavior, and Health (SC2), funded by the Excellence actions program of the University of Granada. The funding organizations had no role in the design or conduct of this research. Research by LLDS is supported by the Ramón y Cajal fellowship program from the Spanish State Research Agency (RYC-2015-17483)