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

DETEKSI KELELAHAN MENTAL DENGAN MENGGUNAKAN SINYAL EEG SATU KANAL

Kondisi kelelahan mental dapat menimbulkan kecelakaan kerja khususnya pada bidang pekerjaan dengan tingkat konsentrasi tinggi. Kondisi ini perlu ditangani dengan serius untuk menghindari risiko kecelakaan kerja. Banyak metode dikembangkan untuk mengukur tingkat kelelahan mental. Namun pengukuran fisiologis dianggap lebih obyektif dan akurat. Sinyal gelombang otak atau electroencephalogram (EEG) merupakan biosinyal yang digunakan sebagai alat ukur fisiologis. Akan tetapi, pemanfaatannya belum banyak diteliti. Penelitian ini memanfaatkan sinyal EEG satu kanal dikombinasikan dengan segmentasi untuk mendeteksi kondisi kelelahan mental. Ciri mean absolute value (MAV), absolute power (AVP), dan standar deviasi (SD) diambil dari setiap segmen. Algoritma klasifikasi Linear Discriminant Analysis (LDA) dan Support Vector Machine (SVM) digunakan untuk mengklasifikasikan kondisi kelelahan mental. Hasil penelitian didapatkan nilai akurasi sebesar 78,13%. Nilai tersebut didapatkan dengan memanfaatkan sinyal EEG dengan segmentasi 60 detik menggunakan Fisher LDA. Penelitian ini menunjukkan sinyal EEG dapat digunakan untuk mendeteksi kondisi kelelahan mental dengan baik meskipun menggunakan ekstraksi ciri sederhana

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

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)

Sistemas de detección de somnolencia en conductores: inicio, desarrollo y futuro

Traffic and industrial accidents are caused by many different factors. Some are due to human error and others, mechanical failure. In an effort to protect lives, many systems have been invented to minimize the impact of such accidents; however, prevention is now understood as more important than minimizing damage after the accident has already taken place. Among the most common human errors that lead to accidents is when a driver or industrial operator is overtired, fatigued or feeling drowsy. Research on this subject began 60 years ago and has developed numerous innovative time systems for detecting states of drowsiness in people using computer vision techniques. There has also been a rising interest in the analysis of brain signals that very precisely determine the different stages of sleep. This paper will review each of the techniques used to detect drowsiness and their importance as active prevention systems for traffic and industrial accidents.Muchas son las causas de accidentes de tránsito e industriales a nivel mundial, algunas de estos suceden por errores humanos y otros por fallas mecánicas. El hombre en su afán de proteger vidas, ha inventado sistemas que minimicen el impacto de estos accidentes, pero más que disminuir el daño ahora se piensa en la prevención de los mismos. Uno de los errores humanos más comunes que terminan en accidentes es cuando el conductor u operario industrial es víctima de fatiga y/o somnolencia. Las investigaciones sobre este tema que comenzaron hace 60 años han dejado a través del tiempo novedosos sistemas que permiten determinar el estado de somnolencia de las personas usando técnicas de visión por computador, y un naciente interés por el análisis de señales cerebrales que determinan de forma aún más precisa las diferentes etapas del sueño. En este trabajo se presentan cada una de las técnicas empleadas hasta el momento para detectar somnolencia y su importancia como sistema activo de prevención de accidentes de tránsito e industriales

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

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

Associating Vehicles Automation With Drivers Functional State Assessment Systems: A Challenge for Road Safety in the Future

In the near future, vehicles will gradually gain more autonomous functionalities. Drivers’ activity will be less about driving than about monitoring intelligent systems to which driving action will be delegated. Road safety, therefore, remains dependent on the human factor and we should identify the limits beyond which driver’s functional state (DFS) may no longer be able to ensure safety. Depending on the level of automation, estimating the DFS may have different targets, e.g., assessing driver’s situation awareness in lower levels of automation and his ability to respond to emerging hazard or assessing driver’s ability to monitor the vehicle performing operational tasks in higher levels of automation. Unfitted DFS (e.g., drowsiness) may impact the driver ability respond to taking over abilities. This paper reviews the most appropriate psychophysiological indices in naturalistic driving while considering the DFS through exogenous sensors, providing the more efficient trade-off between reliability and intrusiveness. The DFS also originates from kinematic data of the vehicle, thus providing information that indirectly relates to drivers behavior. The whole data should be synchronously processed, providing a diagnosis on the DFS, and bringing it to the attention of the decision maker in real time. Next, making the information available can be permanent or intermittent (or even undelivered), and may also depend on the automation level. Such interface can include recommendations for decision support or simply give neutral instruction. Mapping of relevant psychophysiological and behavioral indicators for DFS will enable practitioners and researchers provide reliable estimates, fitted to the level of automation

Human Factors and Neurophysiological Metrics in Air Traffic Control: a Critical Review

International audienceThis article provides the reader a focused and organised review of the research progresses on neurophysiological indicators, also called “neurometrics”, to show how neurometrics could effectively address some of the most important Human Factors (HFs) needs in the Air Traffic Management (ATM) field. The state of the art on the most involved HFs and related cognitive processes (e.g. mental workload, cognitive training) is presented together with examples of possible applications in the current and future ATM scenarios, in order to better understand and highlight the available opportunities of such neuroscientific applications. Furthermore, the paper will discuss the potential enhancement that further research and development activities could bring to the efficiency and safety of the ATM service

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