Neurophysiological determinants of occupational stress and burnout

IntroductionResearch results show that one of the greatest health challenges of the 21st century, especially in developed countries, is becoming the fight against the effects of living too fast, including the fight against occupational stress and burnout. Aim of the study The purpose of this article is to elucidate the neurophysiological determinants of occupational stress and burnout, including ocupational, including through the path of research review and the development of computational models based on artificial intelligence. Materials and methods A literature search was conducted in six bibliographic databases: PubMed, EBSCO, PEDro, Web of Science, Scopus and Google Scholar. Articles were searched in English using the following keywords: occupational stress, burnout, marker, electroencephalography, EEG, magnetic resonance imaging, MRI, fMRI, computed tomography, CT, positron emission tomography, PET, computational model, machine learning, artificial intelligence, virtual patient, digital twin and similar. Neurophysiological determinants of occupational stress and burnout as far as computational models of occupational stress and burnout were analysed and discussed. Results The best currently observed neurophysiological markers of occupational stress and burnout may currently be a combination of EEG analysis (alpha power (IAF, PAF), P300, ERP (VPP and EPN)), diagnostic PET imaging (ACC, insular cortex and hippocampus) and monitoring changes in cortisol, prolactin, adrenocorticotropic hormone (ACTH), corticotropin-releasing hormone (CRH) and thyroid hormones, as well as plasma BDNF levels. In addition, ERPs (LPPs) are a marker significantly differentiating burnout from depression. Conclusions The combination of traditional clinimetric tests, the aforementioned neurophysiological tests and AI-based big data analysis will provide new classifiers, highly accurate results and new diagnostic methods. &nbsp

Framework for Electroencephalography-based Evaluation of User Experience

Measuring brain activity with electroencephalography (EEG) is mature enough to assess mental states. Combined with existing methods, such tool can be used to strengthen the understanding of user experience. We contribute a set of methods to estimate continuously the user's mental workload, attention and recognition of interaction errors during different interaction tasks. We validate these measures on a controlled virtual environment and show how they can be used to compare different interaction techniques or devices, by comparing here a keyboard and a touch-based interface. Thanks to such a framework, EEG becomes a promising method to improve the overall usability of complex computer systems.Comment: in ACM. CHI '16 - SIGCHI Conference on Human Factors in Computing System, May 2016, San Jose, United State

Current Trends in the Application of EEG in Neuromarketing: A Bibliometric Analysis

Despite several neuroscience tools existing, electroencephalography (EEG) is the most used and favoured tool among researchers because of its relatively low cost and high temporal resolution. Our study aimed to identify the global academic research trends of the empirical EEG studies in neuromarketing. This paper adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol to identify relevant articles. A bibliometric analysis software (VOSviewer) was used to evaluate thirty open-access articles found in the Scopus database between 2016 and 2020. We found that the USA is the most productive country with five research articles that used the EEG tool in marketing studies, followed by Australia, Italy, and Malaysia with three articles each. According to the most prolific journals in neuromarketing, it has been found that Frontiers in Neuroscience journal (CiteScore 5.4) is the most prolific journal with two articles and 25 total citations, followed by Scientific reports (CiteScore 7.1) with two articles and eighteen total citations, which lead us to infer that the publications’ number does not necessarily reflect the citations’ number. The study provides a profound and comprehensive overview of academic research that used EEG in marketing research

Learning Optimal Biomarker-Guided Treatment Policy for Chronic Disorders

Electroencephalogram (EEG) provides noninvasive measures of brain activity and is found to be valuable for diagnosis of some chronic disorders. Specifically, pre-treatment EEG signals in alpha and theta frequency bands have demonstrated some association with anti-depressant response, which is well-known to have low response rate. We aim to design an integrated pipeline that improves the response rate of major depressive disorder patients by developing an individualized treatment policy guided by the resting state pre-treatment EEG recordings and other treatment effects modifiers. We first design an innovative automatic site-specific EEG preprocessing pipeline to extract features that possess stronger signals compared with raw data. We then estimate the conditional average treatment effect using causal forests, and use a doubly robust technique to improve the efficiency in the estimation of the average treatment effect. We present evidence of heterogeneity in the treatment effect and the modifying power of EEG features as well as a significant average treatment effect, a result that cannot be obtained by conventional methods. Finally, we employ an efficient policy learning algorithm to learn an optimal depth-2 treatment assignment decision tree and compare its performance with Q-Learning and outcome-weighted learning via simulation studies and an application to a large multi-site, double-blind randomized controlled clinical trial, EMBARC

Object Segmentation in Images using EEG Signals

This paper explores the potential of brain-computer interfaces in segmenting objects from images. Our approach is centered around designing an effective method for displaying the image parts to the users such that they generate measurable brain reactions. When an image region, specifically a block of pixels, is displayed we estimate the probability of the block containing the object of interest using a score based on EEG activity. After several such blocks are displayed, the resulting probability map is binarized and combined with the GrabCut algorithm to segment the image into object and background regions. This study shows that BCI and simple EEG analysis are useful in locating object boundaries in images.Comment: This is a preprint version prior to submission for peer-review of the paper accepted to the 22nd ACM International Conference on Multimedia (November 3-7, 2014, Orlando, Florida, USA) for the High Risk High Reward session. 10 page

Using machine-learning approach to distinguish patients with methamphetamine dependence from healthy subjects in a virtual reality environment

Background: The aim of this study was to evaluate whether machine learning (ML) can be used to distinguish patients with methamphetamine dependence from healthy controls by using their surface electroencephalography (EEG) and galvanic skin response (GSR) in a drug-simulated virtual reality (VR) environment. Methods: A total of 333 participants with methamphetamine (METH) dependence and 332 healthy control subjects were recruited between January 2018 and January 2019. EEG (five electrodes) and GSR signals were collected under four VR environments: one neutral scenario and three METH-simulated scenarios. Three ML classification techniques were evaluated: random forest (RF), support vector machine (SVM), and logistic regression (LR). Results: The MANOVA showed no interaction effects among the two subject groups and the 4 VR scenarios. Taking patient groups as the main effect, the METH user group had significantly lower GSR, lower EEG power in delta (p < .001), and alpha bands (p < .001) than healthy subjects. The EEG power of beta band (p < .001) and gamma band (p < .001) was significantly higher in METH group than the control group. Taking the VR scenarios (Neutral versus METH‐VR) as the main effects, the GSR, EEG power in delta, theta, and alpha bands in neutral scenario were significantly higher than in the METH‐VR scenario (p < .001). The LR algorithm showed the highest specificity and sensitivity in distinguishing methamphetamine‐dependent patients from healthy controls. Conclusion: The study shows the potential of using machine learning to distinguish methamphetamine-dependent patients from healthy subjects by using EEG and GSR data. The LR algorithm shows the best performance comparing with SVM and RF algorithm