Abstract

Recent developments in neurophysiological sensor technologies have enabled the development of wearable and portable devices, which have led to the inception of new research areas such as neuroergonomics emphasizing the utilization of such technologies in the field. The increasing employment of such methods for the monitoring of operators’ physical and cognitive states during real work settings and simulator training scenarios have also brought various technical and methodological challenges for the development and deployment of passive-BCI systems. Such systems are expected to monitor the behavioral and cognitive performance of the operators, and assess their learning needs in an effort to optimize and personalize their simulator training. The design of appropriate task scenarios, development of task performance models to facilitate assessment, individual differences among operators, ensuring good data quality and data synchronization, developing methods for processing and cross-checking of signals originating from different parts of the body and finding appropriate ways to fuse neurophysiological data with machine learning and AI techniques are among these challenges. In the context of our ongoing work with flight simulator training of fighter pilots, we would like to share some of our findings regarding these issues with the hope of establishing connections with EU researchers interested in similar applications of BCI.