Modelling EEG Dataset for Stress State Recognition using Decision Tree Approach

Electroencephalography (EEG) is a predominant tool for learning the stress behavior. This work concentrates towards stress detection by means of eye states. This work proposes a framework which would be supportive in identifying human stress level and as an outcome, distinguishes a normal or stressed person. In this work, we used decision trees, carried out the performance analysis and found that it gives good performance in recognizing the stress states. This analysis is performed with reference to eye state: whether eyes are closed indicating rest, open eyes with blinks

Preliminary study for the measurement of Biosignals in Driving Simulators

openThis preliminary study focuses on the goal of developing and testing a setup and method for non-invasive monitoring of individuals using biosensors in a professional driving simulator (VI-grade Compact Simulator). This involves the synchronization and integration of hardware and software components. To detect the emotional and cognitive state of the driver, it is crucial to identify which signals provide reliable information about their condition. The objective of this study is to observe individuals in a controlled and repeatable environment designed to stimulate cognitive workload. This was achieved using a multimodal assessment method (iMotions), which includes eye tracking, galvanic skin response (GSR), electromyography (EMG), and respiration measurements, all conducted during two distinct controlled driving simulation scenarios. Four healthy subjects (average age = 24, standard deviation = ±2) were monitored during the first scenario, a highway with repeated emergency maneuvers (slalom through cones and double lane change), and the second, five laps of the Paul Ricard circuit. All of this for a total duration of approximately 20 minutes. The participants were not aware that the scenarios were designed to provoke different reactions. This experimental thesis aims to be the continuation and evolution of a testing phase previously conducted during an internship at iMotions, a company that develops multimodal streaming software and distributes commercial hardware. The hardware was supplied to the NAVLAB at the University of Padua, where the simulator is located. The results obtained, at first analysis, appear to be consistent with the literature, suggesting that a multimodal approach to physiological signals may characterize emotional and cognitive states in driving scenarios.This preliminary study focuses on the goal of developing and testing a setup and method for non-invasive monitoring of individuals using biosensors in a professional driving simulator (VI-grade Compact Simulator). This involves the synchronization and integration of hardware and software components. To detect the emotional and cognitive state of the driver, it is crucial to identify which signals provide reliable information about their condition. The objective of this study is to observe individuals in a controlled and repeatable environment designed to stimulate cognitive workload. This was achieved using a multimodal assessment method (iMotions), which includes eye tracking, galvanic skin response (GSR), electromyography (EMG), and respiration measurements, all conducted during two distinct controlled driving simulation scenarios. Four healthy subjects (average age = 24, standard deviation = ±2) were monitored during the first scenario, a highway with repeated emergency maneuvers (slalom through cones and double lane change), and the second, five laps of the Paul Ricard circuit. All of this for a total duration of approximately 20 minutes. The participants were not aware that the scenarios were designed to provoke different reactions. This experimental thesis aims to be the continuation and evolution of a testing phase previously conducted during an internship at iMotions, a company that develops multimodal streaming software and distributes commercial hardware. The hardware was supplied to the NAVLAB at the University of Padua, where the simulator is located. The results obtained, at first analysis, appear to be consistent with the literature, suggesting that a multimodal approach to physiological signals may characterize emotional and cognitive states in driving scenarios

Medical students' cognitive load in volumetric image interpretation:Insights from human-computer interaction and eye movements

Medical image interpretation is moving from using 2D- to volumetric images, thereby changing the cognitive and perceptual processes involved. This is expected to affect medical students' experienced cognitive load, while learning image interpretation skills. With two studies this explorative research investigated whether measures inherent to image interpretation, i.e. human-computer interaction and eye tracking, relate to cognitive load. Subsequently, it investigated effects of volumetric image interpretation on second-year medical students' cognitive load. Study 1 measured human-computer interactions of participants during two volumetric image interpretation tasks. Using structural equation modelling, the latent variable 'volumetric image information' was identified from the data, which significantly predicted self-reported mental effort as a measure of cognitive load. Study 2 measured participants' eye movements during multiple 2D and volumetric image interpretation tasks. Multilevel analysis showed that time to locate a relevant structure in an image was significantly related to pupil dilation, as a proxy for cognitive load. It is discussed how combining human-computer interaction and eye tracking allows for comprehensive measurement of cognitive load. Combining such measures in a single model would allow for disentangling unique sources of cognitive load, leading to recommendations for implementation of volumetric image interpretation in the medical education curriculum

The tiny effects of respiratory masks on physiological, subjective, and behavioral measures under mental load in a randomized controlled trial

Since the outbreak of the coronavirus disease (COVID-19), face coverings are recommended to diminish person-to-person transmission of the SARS-CoV-2 virus. Some public debates concern claims regarding risks caused by wearing face masks, like, e.g., decreased blood oxygen levels and impaired cognitive capabilities. The present, pre-registered study aims to contribute clarity by delivering a direct comparison of wearing an N95 respirator and wearing no face covering. We focused on a demanding situation to show that cognitive efficacy and individual states are equivalent in both conditions. We conducted a randomized-controlled crossover trial with 44 participants. Participants performed the task while wearing an N95 FFR versus wearing none. We measured physiological (blood oxygen saturation and heart rate variability), behavioral (parameters of performance in the task), and subjective (perceived mental load) data to substantiate our assumption as broadly as possible. We analyzed data regarding both statistical equivalence and differences. All of the investigated dimensions showed statistical equivalence given our pre-registered equivalence boundaries. None of the dimensions showed a significant difference between wearing an FFR and not wearing an FFR.TU Berlin, Open-Access-Mittel – 202

An Immersive Virtual Reality Game for Predicting Risk Taking through the Use of Implicit Measures

[EN] The tool presented in this article can be applied as an ecological measure for evaluating decision-making processes in risky situations. It can be used in different contexts from both Occupational Safety and Health practices and for research purposes. Risk taking (RT) measurement constitutes a challenge for researchers and practitioners and has been addressed from different perspectives. Personality traits and temperamental aspects such as sensation seeking and impulsivity influence the individual's approach to RT, prompting risk-seeking or risk-aversion behaviors. Virtual reality has emerged as a suitable tool for RT measurement, since it enables the exposure of a person to realistic risks, allowing embodied interactions, the application of stealth assessment techniques and physiological real-time measurement. In this article, we present the assessment on decision making in risk environments (AEMIN) tool, as an enhanced version of the spheres and shield maze task, a previous tool developed by the authors. The main aim of this article is to study whether it is possible is to discriminate participants with high versus low scores in the measures of personality, sensation seeking and impulsivity, through their behaviors and physiological responses during playing AEMIN. Applying machine learning methods to the dataset we explored: (a) if through these data it is possible to discriminate between the two populations in each variable; and (b) which parameters better discriminate between the two populations in each variable. The results support the use of AEMIN as an ecological assessment tool to measure RT, since it brings to light behaviors that allow to classify the subjects into high/low risk-related psychological constructs. Regarding physiological measures, galvanic skin response seems to be less salient in prediction models.This research was funded by the Spanish Ministry of Economy and Competitiveness funded project "Assessment and Training on Decision Making in Risk Environments", grant number RTC-2017-6523-6, by the Gerenaliat Valenciana funded project "Rebrand", grant number PROMETEU/2019/105, and by the European Union ERDF (European Regional Development Fund) program of the Valencian Community 2014-2020 funded project "Interfaces de realidad mixta aplicada a salud y toma de decisiones", grant number IDIFEDER/2018/029.Juan-Ripoll, CD.; Llanes-Jurado, J.; Chicchi-Giglioli, IA.; Marín-Morales, J.; Alcañiz Raya, ML. (2021). An Immersive Virtual Reality Game for Predicting Risk Taking through the Use of Implicit Measures. Applied Sciences. 11(2):1-21. https://doi.org/10.3390/app11020825S12111

An Immersive Virtual Reality Game for Predicting Risk Taking through the Use of Implicit Measures

Featured ApplicationThe tool presented in this article can be applied as an ecological measure for evaluating decision-making processes in risky situations. It can be used in different contexts from both Occupational Safety and Health practices and for research purposes.Risk taking (RT) measurement constitutes a challenge for researchers and practitioners and has been addressed from different perspectives. Personality traits and temperamental aspects such as sensation seeking and impulsivity influence the individual's approach to RT, prompting risk-seeking or risk-aversion behaviors. Virtual reality has emerged as a suitable tool for RT measurement, since it enables the exposure of a person to realistic risks, allowing embodied interactions, the application of stealth assessment techniques and physiological real-time measurement. In this article, we present the assessment on decision making in risk environments (AEMIN) tool, as an enhanced version of the spheres and shield maze task, a previous tool developed by the authors. The main aim of this article is to study whether it is possible is to discriminate participants with high versus low scores in the measures of personality, sensation seeking and impulsivity, through their behaviors and physiological responses during playing AEMIN. Applying machine learning methods to the dataset we explored: (a) if through these data it is possible to discriminate between the two populations in each variable; and (b) which parameters better discriminate between the two populations in each variable. The results support the use of AEMIN as an ecological assessment tool to measure RT, since it brings to light behaviors that allow to classify the subjects into high/low risk-related psychological constructs. Regarding physiological measures, galvanic skin response seems to be less salient in prediction models

Detecting users’ cognitive load by galvanic skin response with affective interference

Experiencing high cognitive load during complex and demanding tasks results in performance reduction, stress, and errors. However, these could be prevented by a system capable of constantly monitoring users’ cognitive load fluctuations and adjusting its interactions accordingly. Physiological data and behaviors have been found to be suitable measures of cognitive load and are now available in many consumer devices. An advantage of these measures over subjective and performance-based methods is that they are captured in real time and implicitly while the user interacts with the system, which makes them suitable for real-world applications. On the other hand, emotion interference can change physiological responses and make accurate cognitive load measurement more challenging. In this work, we have studied six galvanic skin response (GSR) features in detection of four cognitive load levels with the interference of emotions. The data was derived from two arithmetic experiments and emotions were induced by displaying pleasant and unpleasant pictures in the background. Two types of classifiers were applied to detect cognitive load levels. Results from both studies indicate that the features explored can detect four and two cognitive load levels with high accuracy even under emotional changes. More specifically, rise duration and accumulative GSR are the common best features in all situations, having the highest accuracy especially in the presence of emotions

Trends in Electrodermal Activity, Heart Rate and Temperature during Distracted Driving among Young Novice Drivers.

Driver distraction, defined as the scattering of attention from critical activities for safe driving, is among the key globally recognized contributing factors to road crashes. The trend keeps increasing with in-vehicle information systems and hand-held devices, leading to inattention. Of people in all age groups, young novice teenagers are prone to the risk of road crashes and are also more likely to exhibit risky and unsafe driving behavior. Data shows that the involvement of distracted drivers in fatal & injury collisions is higher for people aged between 16 -34, which is about 55%. Therefore, young drivers are of great concern for the research about driving and evaluation of safe driving conditions, which is vital in upcoming advancements in autonomous vehicles. Several research studies have explored the effects of distracted driving using face tracking and eye glance monitoring. Previous research [50] did not consider much about the effect of distraction on physiological factors and their impact during driving. The current study used data collected from a previous thesis work titled “Detection of Driver Cognitive Distraction Using Machine Learning Methods” by Apurva Misra and conducted new data analysis focusing on new research questions. The main objective of this thesis is to study, identify and discuss the effects on physiological factors like heart rate (HR), electrodermal activity (EDA), body temperature, and motion sickness during distracted driving among young drivers. The data was collected from a driving simulator study comprising 42 participants aged 16 – 23 under normal and distracted driving conditions. Their driving experience ranges from 0 to a maximum of 5 years. Each participant navigated six scenarios, three with distraction and the rest without distraction. Each scenario has a hidden, latent hazard depending on the surrounding; for example, in the work zone scenario, a worker is hidden behind the bulldozer in the work zone. The distraction task is a spoken task for which the driver has to respond verbally, which exerts a workload similar to that observed in conversations using a hands-free mobile phone. The physiological data collected through the Empatica4 wristband was analyzed and compared against age, gender, driver experience, and another parameter like motion sickness score (MSS) obtained from a questionnaire the participants completed after the experiment. Of the physiological factors stated above, it was found that HR and EDA play a significant role while studying distraction. Data analysis showed that HR and EDA increase more during distraction than baseline events. Nearly 80% of drivers with 0 or 1 year of experience tend to have a higher range of HR and EDA, which reveals that they are more distracted than their peers with more experience. From the results of the Load index questionnaire and Motion Sickness susceptibility questionnaire, it is inferred that when MSS increases, there is an increase in HR and EDA. These findings will provide insights into physiological factors for developing distraction mitigation systems or in-vehicle warning systems for distracted drivers

Characterization and Identification of Distraction During Naturalistic Driving Using Wearable Non-Intrusive Physiological Measure of Galvanic Skin Responses

Fatalities due to road accidents are mainly caused by distracted driving. Driving demands continuous attention of the driver. Certain levels of distraction while driving can cause the driver to lose his/her attention which might lead to a fatal accident. Thus, early detection of distraction will help reduce the number of accidents. Several researches have been conducted for automatic detection of driver distraction. Many previous approaches have employed camera-based techniques. However, these methods might detect the distraction rather late to warn the drivers. Although neurophysiological signals using Electroencephalography (EEG) have shown to be another reliable indicator of distraction, EEG signals are very complex, and the technology is intrusive to the drivers, which creates serious doubt for its implementation. In this thesis we investigate a non-intrusive physiological measure-Galvanic Skin Responses (GSR) using a wrist band wearable and conduct an empirical characterization of driver GSR signals during a naturalistic driving experiment. The proposed method is used to evaluate and extract statistical, frequency and time domain features to identify distraction. Also, several data mining techniques such as feature selection, feature-ranking, dimensionality reduction and feature space analysis are performed to generate discriminative bases that reduce the computational complexity for efficient identification of distraction using supervised learning. A signal processing technique: continuous decomposition analysis, exclusive for skin conductance signal was investigated to better understand the behavior of raw signal during cognitive and visual over load from secondary tasks while driving. The proposed driver monitoring and identification system on the edge provided evident results using GSR as a reliable indicator of driver distraction while meeting the requirement of early notification of distraction state to driver.Master of ScienceComputer and Information Science, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/143521/1/Vikas Final Text Embedded.pdfDescription of Vikas Final Text Embedded.pdf : Thesi