Dynamic cognitive workload assessment for fighter pilots in simulated fighter aircraft environment using EEG

• Published in: Biomedical signal processing and control 2020-08, Vol.61, p.102018, Article 102018
• Main Authors: K, Mohanavelu, S, Poonguzhali, K, Adalarasu, D, Ravi, Chinnadurai, Vijayakumar, S, Vinutha, K, Ramachandran, Jayaraman, Srinivasan
• Format: Article
• Language: English
• Subjects: Cognitive dynamic workload; Cognitive performance index; EEG; Fighter aircraft pilots; NASA-TLX; sLORETA
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2020.102018

•The dynamic workload of fighter aircraft across different flying conditions in the simulator environment assessed by EEG signal.•NASA-TLX was carried out for pilot’s attention monitoring.•sLORETA was derived from EEG to understand the relationship of workload and neuronal activity engagement.•This shall be used for pilot’s assessment in their training program. The trend in aviation automation demands more mental workload of pilots, in addition to their routine manoeuvring task. For fighter aircraft pilots, the mental workload multifold due to read-back or hear-back with precise weapon handling in combat scenarios. Assessing the pilot cognitive workload is an important aspect, as it influences the pilot performance, and this is an error intolerance environment. This study aims to assess fighter aircraft Pilot’s Cognitive WorkLoad (PCWL) and attention when they exposed to a dynamic workload environment; by monitoring the neuronal activities. Here the spectral Electroencephalographic (EEG) features were extracted to assess the dynamic workload (normal, moderate, high, and very high workload), and attention monitored by National Aeronautics and Space Administration-Task Load Index (NASA-TLX) and it serves as a validation for cognitive findings. Also, brain source localization obtained from EEG inverse calculation technique, Standardized Low-Resolution brain Electromagnetic Tomography (sLORETA) performed to understand the coherence of workload and neuronal activity engagement concerning brain lobes. Statistical significance (p < 0.05) of EEG feature sets indicate that the approach and withdrawal behaviour of pilots are getting modulated during the different workload of the flying conditions. Further, NASA-TLX findings are well correlated and indicate a cognitive demand during high and very high workload. Furthermore, EEG findings correlated with the sLORETA study, which shows the engagement of prefrontal sensorimotor, fronto-temporal, and parietal regions during various flying conditions, under different cognitive loading. In the future, these findings could be adopted for pilot assessment and as a validation tool of the pilot's training program.