## 3 Results
### Physiological Assessment of Mental Workload
#### 3.6.1 Central Nervous Measures
It was mostly followed by a positive peak around 300 ms after the error onset, and this error positivity (Pe) is related to the user's error awareness. In their study, prediction error negativity (PEN) was measured as the maximum error negativity amplitude in the 100-350 ms window. In settings with active movement of participants, the prediction error positivity (Pe) was measured as the maximum error positivity in the 350-450 ms window. PEN was observed during the execution of a motor task prior to any external error indication. Pe was used to study error awareness, and the study concluded that increased workload depreciates the human awareness of the human operator. The study used error-related potentials (ERP), specifically prediction error negativity (PEN) and error positivity (Pe), to evaluate error awareness during physical human-robot collaboration. PEN, a negative peak around 200 ms after an error occurrence, and Pe, a positive peak around 300 ms, were analyzed under different levels of mental workload. The results showed that the amplitude of PEN and Pe decreased with increasing mental workload, indicating reduced error awareness. Regarding EEG limitations, EEG devices are considered highly sensitive instruments that frequently capture substantial levels of noise, which can be categorized into intrinsic (originating from physiological processes such as facial movements, body motion, and ocular activity) and extrinsic (stemming from environmental or physical sources, such as the existence of background noise or electrical currents) [32; 37]. This noise significantly compromises the quality of EEG recordings. Moreover, EEG signals are non-stationary, which can affect classifier accuracy over time, making it challenging to generalize cognitive workload prediction models [33]. Another critical limitation is the variability in neurocognitive responses among participants, which may influence data interpretation and the reliability of inferred conclusions [36; 49]. Additionally, participants' physical characteristics, such as thick or curly hair, can hinder proper electrode and optode contact, compromising data quality [42]. Lastly, inconsistencies in electrode placement during data collection can introduce signal variations, reducing the robustness of EEG-based models [37]. Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technique that measures the hemodynamic response of the brain. This technique measures changes in oxy- and deoxy-hemoglobin concentration in specific brain regions in contrast to a baseline period. fNIRS detects the hemodynamic changes in response to a cognitive stimulus, allowing the localization of brain responses to specific cortical regions [57]. To assess mental workload, this technique may allow researchers to verify if there is an increase in neural efficiency and functional connectivity in frontoparietal networks, which are involved in executive function and working memory workload [58]. Increased levels of oxy-hemoglobin and decreased deoxy-hemoglobin concentrations, in contrast to baseline measurements, are indicative of significant brain activations. Reduced brain oxygenation is indicative of fatigue, whereas elevated cerebral oxygenation is linked to mental stress and workload [28]. According to [44], HRC resulted in a decrease in oxy-hemoglobin (i.e., reduced activation) in the prefrontal cortex when compared to human-human collaboration and solo work. This led the authors to suggest that HRC may have reduced the participant's mental workload. However, one significant drawback of fNIRS is that its temporal resolution is constrained by the time course of hemodynamic activity (on the order of seconds), as it depends on measuring the absorption characteristics of light as a function of vascular alterations in the brain. To facilitate real-world monitoring in ecologically sound environments, recent work has proposed the simultaneous collection of fNIRS and EEG signals [57; 59; 60]. Ref.