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The human mind is multimodal. Most behavioral studies rely on century-old measures such as task accuracy and latency. To better understand human behavior and brain functionality, we need to analyze physiological and behavioral signals of various sources. However, it is technically complex and costly to design and implement experiments that record multiple measures. To address this issue, a platform that synchronizes multiple measures is needed. This paper introduces an open-source platform named OpenSync, which can be used to synchronize numerous measures in neuroscience experiments. This platform helps to automatically integrate, synchronize and record physiological and behavioral signals (e.g., electroencephalogram (EEG), galvanic skin response (GSR), eye-tracking, body motion, etc.), user input response (e.g., from mouse, keyboard, joystick, etc.), and task-related information (stimulus markers). In this paper, we explain the features of OpenSync and provide two case studies in PsychoPy and Unity. Unlike proprietary systems (e.g., iMotions), OpenSync is free and easy to implement, and it can be used inside any open-source experiment design software (e.g., PsychoPy, OpenSesame, Unity, etc., https://pypi.org/project/OpenSync/ and https://github.com/TAMUCogLab/OpenSync). Our experimental results show that the OpenSync platform is able to synchronize multiple measures with microsecond resolution. • Opensource platform to synchronize multiple measures in neuroscience experiments. • Automatically record physiological measures, user input and task markers. • Guidelines and source files for easy extension to add other devices to the platform. • Can be integrated in opensource stimulus presentation software (e.g., PsychoPy, Unity, etc.). [ABSTRACT FROM AUTHOR]

Background: Mental stress and its consequent mental health disorders (MDs) constitute a significant public health issue. With the advent of machine learning (ML), there is potential to harness computational techniques for better understanding and addressing mental stress and MDs. This comprehensive review seeks to elucidate the current ML methodologies used in this domain to pave the way for enhanced detection, prediction, and analysis of mental stress and its subsequent MDs., Objective: This review aims to investigate the scope of ML methodologies used in the detection, prediction, and analysis of mental stress and its consequent MDs., Methods: Using a rigorous scoping review process with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, this investigation delves into the latest ML algorithms, preprocessing techniques, and data types used in the context of stress and stress-related MDs., Results: A total of 98 peer-reviewed publications were examined for this review. The findings highlight that support vector machine, neural network, and random forest models consistently exhibited superior accuracy and robustness among all ML algorithms examined. Physiological parameters such as heart rate measurements and skin response are prevalently used as stress predictors due to their rich explanatory information concerning stress and stress-related MDs, as well as the relative ease of data acquisition. The application of dimensionality reduction techniques, including mappings, feature selection, filtering, and noise reduction, is frequently observed as a crucial step preceding the training of ML algorithms., Conclusions: The synthesis of this review identified significant research gaps and outlines future directions for the field. These encompass areas such as model interpretability, model personalization, the incorporation of naturalistic settings, and real-time processing capabilities for the detection and prediction of stress and stress-related MDs., (©Moein Razavi, Samira Ziyadidegan, Ahmadreza Mahmoudzadeh, Saber Kazeminasab, Elaheh Baharlouei, Vahid Janfaza, Reza Jahromi, Farzan Sasangohar. Originally published in JMIR Mental Health (https://mental.jmir.org), 21.08.2024.)

Habituation to signals that warn of a potential danger in high-risk work environments is a critical causal factor of workplace accidents. Such habituation is hard to measure in a real-world setting, and no existing intervention can effectively curb it. Here, we present a protocol to enhance workers' sensory responses to frequently encountered warnings at workplaces using a virtual-reality-based behavioral intervention. We describe steps for performing a virtual reality experiment and an electroencephalography (EEG) experiment with human participants. For complete details on the use and execution of this protocol, please refer to Kim et al. 1 ., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

In high-risk work environments, workers become habituated to hazards they frequently encounter, subsequently underestimating risk and engaging in unsafe behaviors. This phenomenon has been termed "risk habituation" and identified as a vital root cause of fatalities and injuries at workplaces. Providing an effective intervention that curbs workers' risk habituation is critical in preventing occupational injuries and fatalities. However, there exists no empirically supported intervention for curbing risk habituation. To this end, here we investigated how experiencing an accident in a virtual reality (VR) environment affects workers' risk habituation toward repeatedly exposed workplace hazards. We examined an underlying mechanism of risk habituation at the sensory level and evaluated the effect of the accident intervention through electroencephalography (EEG). The results of pre- and posttreatment analyses indicate experiencing the virtual accident effectively curbs risk habituation at both the behavioral and sensory level. The findings open new vistas for occupational safety training., Competing Interests: The authors declare no competing interests., (© 2022 The Author(s).)