## 4 Discussion  
This systemic review focuses on the topic of how previous studies have assessed mental workload in HRC scenarios. To this end, we collected data from 25 papers of an initial pool of 413 papers. From these 25, information was extracted regarding which tasks they employed to induce mental workload in participants working with robots, as well as what physiological, subjective, and performance data were collected and reported in each paper. From the information collected from each of these papers, we also presented a brief summary regarding each of the metrics that were found to be employed throughout the 25 papers, including information regarding both their strengths and limitations. The importance of mental workload in industries has recently gained recognition due to accumulated knowledge generated in recent years. The studies reviewed in this paper focused on tasks like assembling, blasting, material handling, quality inspection, problem-solving, construction-related tasks, agricultural tasks, pick-and-place, and simultaneous capacity multitasking. These tasks are inherently physically and mentally demanding and thus can benefit from precise assessments of mental workload to ensure a worker's optimal performance, efficiency, and, most importantly, safety. However, the complexity of the human factor, which encompasses multiple variables, as well as the inherent differences between different tasks and environments, among other factors, makes it difficult to develop a worldwide and standardized concept [10]. The diverse measures of mental workload and their interactions further complicate developing a unified idea and also make it challenging to compare among the different studies. The lack of a holistic concept makes it difficult to adopt tried-and-true tactics, which is an obstacle to the creation of management systems. Nonetheless, through the combined use of physiological, subjective, and performance-based measures, researchers are making strides in effectively operationalizing and managing mental workload. Physiological sensors played a crucial role in this process, with brain measures being the most frequently used method (Table 1), as they allow for brain activity to be directly measured. However, despite promising results, defining the specific spectral frequency that best characterizes changes in mental workload, as well as the location of electrodes, remains divergent. Still, several studies have consistently reported increased theta and beta bands located in the frontal lobe (related to executive functions) during periods of elevated mental workload [46]. The alpha band, typically associated with relaxation, showed a significant decrease under increased cognitive load in several studies [36,42,46,49]. While EEG has been widely explored for workload assessment, its potential extends far beyond this application. Given its ability to capture real-time, objective data on cognitive and emotional states, researchers have increasingly investigated its use in other domains. For instance,EEG is being integrated into user experience evaluation, providing valuable insights that complement traditional subjective measures. Recent studies have demonstrated that EEG can effectively reflect user satisfaction and emotional responses during human-robot interactions, offering a deeper understanding of both conscious and subconscious reactions that subjective metrics might fail to capture [75]. Currently, there is a growing trend toward the simultaneous collection of brain signals using EEG and fNIRS, as the two neuroimaging techniques complement each other with the temporal resolution of EEG and the spatial resolution of fNIRS [42]. However, even though optical imaging techniques have a higher spatial resolution than EEG, their ability to draw conclusions is constrained by the shallow penetration depth of NIR light, which is just a few centimeters below the scalp's surface.