Unmanned aerial vehicle (UAV) operating is a complex task performed in a dynamic and uncertain environment. During UAV operation, the human operator is often faced with difficult decisions that have to be made within a limited amount of time and that can result in dramatic consequences. A solution to prevent or palliate the drops in operator performance that can result from such a task is to perform mental state monitoring and to include the collected information in the decision scheme along with the robotic state. In order to design systems that take the human operator into account, the mental states that are relevant for this particular type of task should be described and assessed in ecological settings. Examples of such mental states are the ones related to time-on-task increases -such as fatigue and mind wandering-, and the ones related to mental workload -such as memory load and temporal pressure-, as well as automation surprise. In particular, as regards time-on-task related mental states, as the systems grow more automatized the operators are requested to operate at irregular and inter-spaced intervals
(Cummings et al., 2013). Hence, they can be waiting in a very long and monotonous monitoring phase. Although UAV operators’ fatigue state has already been assessed at the behavioral and oculomotor levels, to our knowledge there is a lack of literature regarding potential cardiac and cerebral markers for this particular application field.