## 4 Discussion
Consequently, there is limited ability to image the activity of deep cortical and subcortical sources (beyond the outer cortical mantle) [42; 44]. Data from cardiac measures, in turn, are also commonly used to evaluate mental workload. HRV, for example, can be used as an indicator of workload [18], as changes to it can reflect responses from the autonomic nervous system [62; 76]. For example, a decrease in HRV can indicate mental workload. Moreover, in the extraction of HRV features, the LF/HF ratio is frequently used to assess mental workload. As mental workload increases, parasympathetic activity decreases, and an increase in the LF/HF ratio is observed [76]. Lastly, feature selection in EDA studies is primarily centered around skin conductance response, as it provides a reliable indicator of the body's response to mental workload [4]. Its capacity to record variations in sympathetic nervous system activity in response to workload fluctuations offers insightful data on the person's level of mental workload while performing a task [72]. Although brain and cardiac measures are dominant, there is a clear trend toward the exploration of integrated variables to assess mental workload in HRC. The study conducted by [77] employed a deep neural network (DNN) model to classify mental workload levels based on EDA, EEG, and PPG signals, achieving a classification accuracy of 86% and highlighting the value of multimodal physiological signals for robust workload detection. The combination of ocular measures with heart rate in [43] to assess cognitive workload during assembly tasks further illustrated the utility of multimodal sensing in accurately assessing mental workload. The increase in the use of more integrated methodologies points towards a more holistic approach to mental workload evaluation in recent years as researchers build upon prior studies. It is important to note that some researchers still advocate for the adoption of alternative or more tailored measures, suggesting that the current trend may not entirely reflect every researcher's perspective. For example, subjective measures have remained important throughout the research timeline, with tools like the NASA-TLX being consistently employed. While physiological measures capture real-time data on brain activity and performance metrics provide task-related outputs, subjective measures offer insights into how individuals perceive their workload. Furthermore, subjective metrics can also be applied to assessing a subject's user experience, affective states, and stress [78], which can also be useful metrics to evaluate other aspects of the human-robot relationship and collaboration. Nevertheless, they frequently overlook the nuances of unintentional emotional reactions or risk distortion brought on by subjective assessments. Thus, the use of facial expressions for detecting cognitive and emotional states has gained prominence in various research fields, such as psychology, marketing, medicine, and human-computer interaction [79]. In terms of performance metrics, key indicators such as task accuracy, reaction time, and error rates are consistently employed across studies to quantify mental workload. For instance, multitasking and safety-critical operations often utilize EEG and HRV in tandem with performance measures to offer a more comprehensive assessment of mental workload [62; 76]. The inclusion of these metrics in both controlled experiments and real-world settings underscores their reliability and relevance to managing workload in operational environments [80]. Despite the promising results of physiological metrics for the assessment of mental workload, several limitations need to be acknowledged. One primary limitation is the small sample size across most of the reviewed studies, which can significantly affect the generalizability of the findings. For instance, in [36], the authors used only two participants, limiting the robustness of the conclusions.