## 5 Discussion
In this context, a decline in relative alpha band power, commonly associated with an escalation in cognitive activation, has been observed as the complexity of an assembly task intensifies. Statistically significant alterations have been documented in the bands power of the fronto-central area during human-robot collaborative tasks. Of particular interest is the finding that a decrease in theta band occurred subsequent to correct decisions by the robotic agent, coinciding with an increase in confidence. EEG spectral features were demonstrated to be a valid means to predict mental load and replace subjective evaluations. An alternative classification approach, incorporating spectral and temporal characteristics of the EEG signal and the phasic component of electrodermal activity, was adopted to predict the level of confidence in real-time, exhibiting a satisfactory level of accuracy. Also, eye tracking shows promise in providing information with respect to a greater number of mental states. Through the analysis of temporal metrics (e.g., fixation duration, blinks), spatial metrics (e.g., saccade amplitude), and counting metrics (e.g., number of fixations), it is possible to assess various aspects of cognitive function, including mental workload, attention, trust, and learning. A notable increase in the frequency of blinks, for instance, can be indicative of an elevated mental workload. In relation to trust, lower levels were associated with higher frequencies and durations of fixations. The integration of various eye-tracking metrics has enabled the development of a TEI engagement index, which serves as a quantitative metric for the level of visual attention. Conversely, pupillary dilation has facilitated the assessment of the learning phases of workers during extended work shifts. A decrease in pupillary response has been shown to be indicative of reduced cognitive load associated with learning. Concurrently, a high number of fixations and saccades were recorded in the initial phase, decreasing over time. In the extant literature, correlates of cardiac and electrodermal activity have been used to a lesser extent, often in a multimodal human factors analysis. The phasic component (SCR) of the electrodermal activity (EDA) signal has been employed for the assessment of workers' stress, as it is known in the literature to be correlated with emotional arousal, anxiety, and stress. During HRC in assembly tasks, heart rate variability demonstrated a correlation with mental workload, such that increased mental fatigue is reflected in a reduction in parasympathetic activity and, consequently, an increase in the LF/HF ratio. Furthermore, LF/HF was capable of detecting significant differences in mental workload between men and women; however, it proved less sensitive than eye-tracking in registering minor fluctuations in mental states. The advantage of the methods employed lies in their good wearability and minimal invasiveness in experimental scenarios, which extends their use to out-of-the-lab application. Specifically, eye-tracking, cardiac, and electrodermal activities are acquired by means of mobile devices such as glasses and bracelets that can also be adapted for use in real-world contexts. With regard to electroencephalography (EEG), the majority of devices under consideration are wireless systems with a low electrode density, easily transferable to real operating contexts. The previous findings emphasize the efficacy and potential of evaluating human factors in Industry 5.0 through the use of neurophysiological measures. Nevertheless, the intricate interconnection among disparate mental states, which has led researchers to prefer a multidimensional approach, complicates the isolation of distinct components, as these are not readily discernible without subsequent correlation with subjective assessments. Consequently, there is an absence of a consistent characterization of factors such as learning and mental stress, which are directly associated with mental workload and fatigue.