## 5 Discussion  
This review emphasized the critical importance of neurophysiological methodologies, in contemporary scientific research, for the characterization of human factors in Industry 5.0. Human factors assume considerable importance in a context that aspires to a human-centric, sustainable, and resilient vision of workplaces where human-machine interaction is implemented to enhance productivity and efficiency. The continuous monitoring of workers' well-being through the analysis of these human factors is fundamental in addressing individual needs, thereby fostering a more human and inclusive work environment. Furthermore, the assessment of workers' mental states and responses facilitates the development of more intuitive interfaces and intelligent systems that facilitate mutual adaptation. In this regard, it must be underlined that incorporating Human factors evaluations during the operational phase not only ensures a safer and more efficient working environment but also fosters resilience by enabling systems to adapt dynamically to human limitations and capacities. This approach ultimately enhances the overall decision-making process, reducing errors and improving outcomes in complex operational settings Consequently, this approach has the potential to generate economic, social, and environmental value. The objective of this study was to identify the predominant neurophysiological methods employed in the analysis of mental states, such as stress, mental workload, attention, trust and learning, in the recent literature. The followings provide a summary of the key points emerged from the presented review:  
* Even if the sensitivity of neurophysiological signals to environmental factors, such as movement artefacts or electrical interference, might limit the applicability of these methods in highly dynamic settings, the advent of miniaturized and wireless sensors provides an avenue for more user-friendly solutions, enabling seamless integration into wearable devices without compromising data quality. * The use of advanced signal processing and machine learning algorithms offers the potential to enhance artefact removal, feature extraction, and real-time interpretation of neurophysiological data. This could also address the current limitations characterizing the neurophysiological signal quality when collected by the actual wearable technologies. Regarding the principal objective of the presented review, these were the key gaps identified in scientific literature:
* Limitation in the context of the real-world validation, since many studies were confined to laboratory environments, which do not fully replicate the complexities of real-life workplaces. More field studies are needed to validate these techniques in diverse occupational settings. * As of now, few studies effectively integrate EEG, EDA, and PPG data in real-world applications. It emerged that the multimodal approaches should be furtherly developed and applied in real-world contexts. * Current research often overlooks the broader organizational and teamwork dynamics. Expanding the focus to include group-level metrics, such as collective stress or shared workload, could provide valuable insights for optimizing team performance. The analysis revealed that the mental states under investigation are often interconnected, sometimes by a cause-effect mechanism, such as workload and stress, so that several factors are analyzed within the same study. Correlation tests with behavioural parameters or the results of questionnaires used for single-factor assessment (e.g., the NASA Task Load Index for assessing mental load) can be used for their discernment. The predominant methodologies employed in the articles under consideration are electroencephalography and eye-tracking. A significant body of research has demonstratedthat the spectral characteristics of the EEG signal, particularly in the prefrontal region, exhibit notable sensitivity to variations in stress and mental load.