## 1 Introduction  
Industry 4.0, often referred to as the fourth industrial revolution, embodies a landscape where digital technologies intertwine with production processes, redefining the very nature of enterprises and human interaction with the surrounding environment [1]. Within this context, the integration of passive Brain-computer Interface (BCI) technologies may enable direct engagement of the user in the productive and/or industrial process [2; 3]. Unlike standard and subjective methods (e.g., questionnaires), these technologies allow for a covert estimation (i.e., without asking anything to the user) of mental and emotional states of the user while engaged in the operational task, allowing for continuous feedback with the surrounding environment to put the user in the loop. One of the most relevant mental states to be mentioned for out-of-the-lab applications, especially in safety-critical environments, is undoubtedly the mental workload [4; 5; 6]. In fact, it has been widely demonstrated that an operator's mental workload level (overload) that is too high could lead to degraded performance and/or an increased chance of committing errors [7]. The concept of mental workload cannot be viewed as singular; rather, it arises from various interrelated aspects. Numerous mental processes, including alertness, vigilance, mental effort, attention, mental fatigue, drowsiness, and more, may come into play during task execution. Moreover, these processes can be influenced at any given moment by the specific demands of the task [8; 9; 10]. The primary purpose of workload measurement is to assess the mental effort involved in task performance, aiming to predict potential declines in operator performance during operational tasks. This approach allows us to gather information about when a task might become overly complex for a participant or when a participant is experiencing cognitive strain [11; 12; 13]. This measure is, therefore, particularly important in safety-critical contexts, where human error (e.g., induced using overload or lack of attention) could induce disastrous consequences (e.g., piloting an airplane or air traffic management) [14; 15]. Numerous studies have highlighted a significant correlation between the amplitude of Electroencephalographic (EEG) signals recorded on frontal and parietal EEG channels and variations in workload levels. Specifically, a significant EEG amplitude difference is observed in the Theta band on frontal channels and the Alpha band on parietal channels. For example, research conducted first by [PERSON] and later by [PERSON] and [PERSON] [16; 17; 18] has demonstrated that variations in EEG signals detected in these cerebral regions are closely linked to changes in mental workload during the execution of working memory tasks. For example, [PERSON] et al. conducted an experiment on 20 participants who were asked to perform the Priority Management Task, currently used by the Italian Civil Aviation Authority (ENAC) for the selection of airline pilots. These findings have been confirmed by a large number of subsequent studies performed in realistic settings [8; 19], paving the way for an out-of-the-lab use of such systems in the near future. In the current scenario, many studies have focused on creating pBCI devices that are more portable and comfortable but, at the same time, reliable [20; 21; 22]. As a result, there could be potential use not only within the confines of the laboratory but also in real-world settings, driving the use of passive BCI models in more practical contexts [23; 24; 25; 26; 27]. However, while every variable can be fully controlled in a laboratory environment, such control cannot be guaranteed in real operational situations [24; 28].