Using EEG to Provide Insights in Construction Safety
Key Highlights
- New research looks at EEG's role in real-time monitoring of workers' cognitive and emotional states and its potential in automating the detection of psychological hazards like stress and fatigue, thereby preventing accidents.
- The integration of EEG with advanced technologies such as robotics, virtual reality, and wearable sensors is highlighted to improve hazard recognition, decision-making, and situational awareness.
- The study addresses critical challenges, including data privacy and system scalability, providing practical insights for implementing EEG-based systems in construction settings.
A recent research paper,"Electroencephalography (EEG) for Psychological Hazards and Mental Health in Construction Safety Automation," looked into how the technology could recognize psychological hazards and monitor mental health in construction safety.
With the increased use of wearable technology, the authors note that EEG systems provide real-time insights into workers' cognitive and emotional states, helping to identify stress, fatigue, and safety risks.
To conduct the research, the authors used a structured search algorithm from the literature from Scopus and Web of Science. It was then filtered and analyzed to create a comprehensive framework for EEG deployment in five key domains: automated psychological and cognitive assessment, hazard recognition and safety decision-making, advanced technology integration, situational awareness enhancement, and sustainability contributions.
The review underscores the synergy of EEG with robotics, virtual reality, and wearable devices, enhancing safety management in construction, the authors say.
The following is the conclusion of the research. (It is excerpted from the paper.)
This paper presented a comprehensive exploration of electroencephalography (EEG) applications in enhancing safety and sustainability within the construction industry. By systematically reviewing EEG's role in real-time monitoring of workers' cognitive and emotional states, the research underscores its potential in automating the detection of psychological hazards like stress and fatigue, thereby preventing accidents.
The integration of EEG with advanced technologies such as robotics, virtual reality, and wearable sensors is highlighted to improve hazard recognition, decision-making, and situational awareness.
Furthermore, the study addresses critical challenges, including data privacy and system scalability, providing practical insights for implementing EEG-based systems in construction settings. This research contributes significantly to the advancement of intelligent, responsive, and sustainable safety management systems in the construction sector.
Building upon this foundation, the study introduces an algorithmic systematic review (ASR) of EEG applications aimed at identifying psychological hazards and monitoring mental health for more sustainable construction safety practices. Employing the Search Paper Algorithm (SPA), the research systematically identifies and analyzes relevant studies from comprehensive databases such as Scopus and Web of Science.
A comprehensive framework is developed, outlining the deployment of EEG systems across five key application domains: automated psychological and cognitive assessment, hazard recognition and safety decision-making, advanced technology integration, situational awareness enhancement, and contributions to sustainability.
The framework emphasizes the synergy between EEG and other innovative technologies, highlighting their roles in promoting safety and sustainable practices within the construction industry.
In exploring human-machine collaboration, the study investigates how EEG-based methods can infer perceptual thresholds and decision confidence, enabling more effective collaboration between humans and machines.
Innovations include the development of brain-computer interfaces and adaptive automated hazard alerting systems that leverage real-time EEG data. These advancements highlight the potential for improved safety outcomes through advanced human-machine interactions.
Consequently, the findings contribute significantly to Industry 5.0 by emphasizing human-centric technologies that enhance worker safety, well-being, and productivity. The integration of EEG into neuro-safety frameworks facilitates proactive safety measures, fosters situational awareness, and advances the construction industry's shift toward more sustainable and intelligent systems.
Looking ahead, future research should focus on conducting large-scale field studies to validate EEG applications in real-world construction environments. Integrating EEG data with other modalities, such as motion tracking and environmental sensors, can provide a more comprehensive understanding of worker states and hazard scenarios.
Developing personalized models tailored to individual differences will enable targeted interventions, while exploring adaptive and closed-loop systems can allow for automatic adjustments to safety interventions based on real-time EEG data and worker feedback. Longitudinal studies are also necessary to investigate the long-term effects of EEG-based interventions on worker safety, productivity, and well-being.
Additionally, incorporating EEG data into augmented reality (AR) and virtual reality (VR) environments can enhance immersive safety training and hazard visualization. Developing hybrid brain-computer interface (BCI) systems that combine EEG with other neuroimaging techniques, such as functional near-infrared spectroscopy (fNIRS), can improve signal quality and information richness.
Exploring the use of explainable artificial intelligence (AI) models will enhance the interpretability and transparency of EEG-based decision-making systems for construction safety. Establishing ethical and regulatory frameworks is crucial to address privacy, data ownership, and potential misuse concerns associated with EEG and neuroimaging technologies in workplace settings.
Finally, fostering cross-industry collaboration between construction, neuroscience, engineering, and computer science will accelerate the development and adoption of EEG-based safety solutions, leveraging interdisciplinary expertise to create cost-effective, scalable systems.