A neurophysiological training evaluation metric for air traffic management

The aim of this work was to analyze the possibility to apply a neuroelectrical cognitive metrics for the evaluation of the training level of subjects during the learning of a task employed by Air Traffic Controllers (ATCos). In particular, the Electroencephalogram (EEG), the Electrocardiogram (ECG) and the Electrooculogram (EOG) signals were gathered from a group of students during the execution of an Air Traffic Management (ATM) task, proposed at three different levels of difficulty. The neuroelectrical results were compared with the subjective perception of the task difficulty obtained by the NASA-TLX questionnaires. From these analyses, we suggest that the integration of information derived from the power spectral density (PSD) of the EEG signals, the heart rate (HR) and the eye-blink rate (EBR) return important quantitative information about the training level of the subjects. In particular, by focusing the analysis on the direct and inverse correlation of the frontal PSD theta (4-7 (Hz)) and HR, and of the parietal PSD alpha (10-12 (Hz)) and EBR, respectively, with the degree of mental and emotive engagement, it is possible to obtain useful information about the training improvement across the training sessions.