The integration of neurophysiological techniques into Industry 5.0 represents a transformative approach to assessing human factors in real-world operational settings. This study presents a systematic review of existing literature to evaluate the application of neurophysiological methods in assessing cognitive and emotional states, such as workload, stress, attention, and trust, within industrial environments. A total of X peer-reviewed articles published between 2018 and 2024 were analyzed following a structured methodology. The findings reveal that EEG (45%), eye-tracking (30%), EDA (20%), and ECG (15%) are the most frequently adopted techniques for monitoring cognitive and emotional responses. Additionally, 60% of the studies focused on stress and workload assessment, while only 25% examined trust and collaboration in human–robot interaction, highlighting a gap in comprehensive teamwork analysis. Furthermore, only 35% of the studies validated their approaches in real-world industrial settings, emphasizing a significant limitation in ecological validity. The review also identifies that multimodal integration remains underexplored, with just 15% of studies combining multiple neurophysiological signals for a more holistic assessment. These results indicate a growing but still fragmented research landscape, with clear opportunities for expanding real-world applications, improving methodological standardization, and fostering interdisciplinary collaboration. Future research should prioritize validation in dynamic, real-life work environments and explore the synergistic potential of multimodal neurophysiological approaches to enhance human-centred industrial systems.

Mixed Reality (MR) environments hold immense potential for inducing a sense of embodiment, where users feel like their bodies are present within the virtual space. This subjective experience has been traditionally assessed using subjective reports and behavioral measures. However, neurophysiological approaches offer unique advantages in objectively characterizing embodiment. This review article explores the current state of the art in utilizing neurophysiological techniques, particularly Electroencephalography (EEG), Photoplethysmography (PPG), and Electrodermal activity (EDA), to investigate the neural and autonomic correlates of embodiment in MR for out-of-the-lab applications. More specifically, it was investigated how EEG, with its high temporal resolution, PPG, and EDA, can capture transient brain activity associated with specific aspects of embodiment, such as visuomotor synchrony, visual feedback of a virtual body, and manipulations of virtual body parts. The potential of such neurophysiological signals to differentiate between subjective experiences of embodiment was discussed, with a particular regard to identify the neural and autonomic markers of early embodiment formation during MR exposure in real settings. Finally, the strengths and limitations of the neurophysiological approach in the context of MR embodiment research were discussed, in order to achieve a more comprehensive understanding of this multifaceted phenomenon.

Background/Objectives: The Human Performance Envelope (HPE) is a multidimen-sional model that represents the range in which an individual operator's performance is acceptable or begins to become dangerous. Although several alternative models have been proposed, HPE currently remains primarily a theoretical concept. The goal of the study was therefore to translate this theoretical concept into practical applications, seeking to characterise and measure how HPE manifests itself in real-world contexts. Methods: Multivariate Autoregressive Models (MVAR) have been used in the analysis of complex systems in which variables are interdependent and mutually influence their dynamics over time. Professional Air Traffic Controllers (ATCOs) were involved in the study and asked to deal with realistic traffic scenarios while their behavioural, subjec-tive and neurophysiological data were collected. Partial Information Decomposition - Least Absolute Shrinkage and Selection Operator (PID – LASSO) model was then em-ployed to estimate the interactions among ATCO’s Human Factors (HFs) and identify the most appropriate characterisation of the HPE. Results: The results showed high and significant correlations among each ATCO’s performance and the corresponding neu-rophysiological – based HPE values. Furthermore, high-performance conditions (Best) were characterized by a significantly higher HPE values and a higher inter-HFs con-nections compared to low-performance (Worst) states. This suggested that a densely interconnected network of HFs is a prerequisite for operational resilience. Conclusions: The study provides the first application of a neurophysiological framework to model the causal interactions between HFs, translating the theoretical HPE into a quantifiable model validated against operator performance.

Background/Objectives: The human performance envelope (HPE) is a multidimensional model that represents the range in which an individual operator’s performance is acceptable or begins to become dangerous. Although several alternative models have been proposed, HPE currently remains primarily a theoretical concept. The goal of the study was therefore to translate this theoretical concept into practical applications, seeking to characterize and measure how HPE manifests itself in real-world contexts. Methods: Multivariate Autoregressive (MVAR) models and conditional transfer entropy (cTE) have been used in the analysis of complex systems in which processes are interdependent and mutually influence their dynamics over time. Professional Air Traffic Controllers were involved in the study and asked to deal with realistic traffic scenarios while their behavioural, subjective and neurophysiological data were collected. MVAR–cTE models were then employed to estimate the interactions among controller human factors and to identify the most appropriate characterization of the HPE. Results: The results showed high and significant correlations among each controller’s performance and the corresponding neurophysiological-based HPE values. Furthermore, high-performance conditions (best) were characterized by significantly higher HPE values and higher inter-human factor connections compared to the low-performance (worst) status. This evidence suggested that a densely interconnected network of Human Factors is a prerequisite for operational resilience. Conclusions: The study provided the first application of a neurophysiological framework to model the directed interactions between human factors, translating the theoretical HPE into a quantifiable model validated against operator performance.