In several domains, such as aviation, sports, health-care, or automotive, human errors could have serious and dramatic consequences; therefore performance and safety of the people rely on the actual psychophysical state of the operator(s). In addition, in many operational environments the role of humans has shifted from total manual control to passive monitoring of autonomous systems operating together (e.g., air traffic control rooms, plants systems monitoring, and autonomous train driving). In all these contexts, the evaluation and the continuous monitoring of the operator represent a fundamental aspect to keep proper performance and safety level. In particular, the human factor and neuroergonomics disciplines aim to evaluate all those components (i.e., hereafter, human factors) of the human psychophysical state, in order to definitely increase human performance at work.

In recent years, the possibility of quantifying, even in real-time, the operator’s human factors (e.g., workload, attention) during his/her working activity has been explored, by using passive Brain-Computer Interface (pBCI) technologies, especially EEG and fNIRS-based. Such information could be then used to change/adapt the behavior of the interface the user is interacting with in order to avoid, or at least mitigate, error commission risk and more in general to improve Human Machine Interaction (HMI).

Although several steps forward have been taken, in terms of both algorithms and technology, there are still several open issues to be addressed to use such pBCI technology outside lab environments.

Therefore, the aim of this special issue is to outline the state of the art regarding neurophysiological measures including pBCI applications in real world settings, with the specific purpose of evaluating human factors, and of the solutions to overcome current issues. In particular, the expected works will be mainly focused on three main topics: i) advancement in new machine learning models able to maintain a high classification accuracy over time without requiring long calibration (or, ideally, no calibration); ii) new ergonomic biosignals recording systems, minimally invasive, highly effective, and immune to noise; iii) experiences performed in high-realistic or real settings proving the reliability and the applicability of the proposed systems. Finally, the authors are encouraged to propose original research papers and review articles describing the current state of the art of the proposed topics.

Potential topics include but are not limited to the following:

• Passive Brain-Computer Interfaces
• Innovative machine learning techniques
• Artifact rejection techniques
• Biosignals wearable recording systems
• Human factors evaluation