## 3 Results
### Physiological Assessment of Mental Workload
#### 3.6.1 Central Nervous Measures  
Central nervous measures have been predominantly used, observed in 13 of the reviewed studies, underscoring the centrality of neurophysiological metrics to capture real-time brain activity and assess mental workload levels during collaborative interactions. Depending on their intended use, neuroimaging methods can be divided into two primary categories: those that aim to measure neural activity indirectly by using metabolic indicators, like fNIRS, to provide a comprehensive picture of cognitive states and those that measure neural activity directly in response to stimuli using an EEG [54]. Electroencephalogram  
The historical trajectory of understanding the brain's electrical activity, initiated by [PERSON] in 1875, has evolved with contemporary technologies like the electroencephalogram. This non-invasive method captures rapid changes in the brain's electrical signals resulting from ion movements within neurons [37, 55]. Employing multiple electrodes on specific scalp regions, often following the 10-20 system [56], EEG unveils standardized rhythmic patterns in alpha (7-12 Hz), theta (4-7 Hz), beta (13-30 Hz), delta (0.5-4 Hz), and gamma (\(>\)30 Hz) frequency bands. These frequency bands correspond to distinct mental processes, such as relaxation, focus, executive control, and cognitive stress. Delta frequency is associated with deep sleep [37, 38]. EEG spectral power analysis has shown consistent patterns of activation and inhibition during HRC tasks, particularly in response to cognitive workload fluctuations. The results indicate a decrease in parietal alpha power as cognitive load increases [36, 46], supporting  
Figure 3: Workload assessment through subjective measures. the notion that alpha activity is linked to relaxation and reduced engagement [42]. Additionally, high-alpha power was more prominent in the maladaptive condition, possibly reflecting an increased effort to inhibit irrelevant information due to robotic errors [49]. Beta power increased in response to heightened cognitive load and task complexity, indicating greater mental effort and alertness during demanding tasks [42]. Theta power increased significantly in the frontal region following unexpected robot stops, particularly under high cognitive load conditions, suggesting heightened working memory demands and executive control during error detection [46]. Additionally, higher theta power was observed in the central and temporal regions when participants performed tasks alone, indicating a greater focus on internal task representation, while in the maladaptive condition (inexperienced robot), theta power increased in the occipital region, reflecting greater visual processing due to robot errors [49]. Furthermore, an increase in frontal theta activity was linked to mental effort, reinforcing its role in working memory activation and executive control [36]. To assess cognitive load, researchers employ EEG-derived ratios and indices that provide insights into attentional focus, mental workload, and cognitive engagement during task execution (Table 2). The Alpha/Theta ratio (\(\alpha/\theta\)) is commonly used to measure attentional focus, with higher values indicating increased cognitive engagement. In experimental conditions, an increase in this ratio suggested enhanced focus and attention during task execution [37]. The Alpha/Beta ratio (\(\alpha/\beta\)), associated with relaxation versus cognitive effort, showed an increase post-task, reflecting reduced stress and a transition to a more relaxed state [37; 41]. Conversely, the Beta/Alpha ratio (\(\beta/\alpha\)), which is indicative of mental workload, demonstrated distinct patterns. In standard conditions (without robotic assistance), it remained stable or increased slightly throughout the task, whereas in a collaborative robot-assisted scenario, a significant reduction was observed, particularly in later task