Identifying neural correlates of cognitive workload in high-performance motorsport simulation: an integrated EEG and telemetry analysis of driver performance

High-performance motorsport requires precise cognitive regulation and rapid decision-making under extreme dynamic conditions, yet traditional vehicle telemetry alone cannot reveal the psychophysiological mechanisms that influence driving performance. This study presents an integrated neuroengineering framework combining electroencephalography (EEG), and vehicle telemetry to identify objective neural markers of cognitive workload, emotional valence, and mental fatigue in a high-fidelity Formula 1 simulation. 15 participants drove on the Silverstone Circuit in the simulation platform, during which physiological data were continuously recorded and synchronized. Performance tiers were classified using k-means clustering on lap times and trajectory consistency, followed by EEG-based analysis of workload and fatigue indices. Results showed that high-performing drivers exhibited efficient workload modulation, higher alertness, and reduced fatigue compared to lower-performing drivers. A track-specific “cognitive workload profile” was also identified, revealing that technically demanding corners induced higher neural workload, whereas moderate turns corresponded to transient engagement peaks. The findings demonstrate that integrating EEG with telemetry enables objective, data-driven assessment of driver cognitive states and provides a foundation for predictive modeling, driver performance optimization, and advanced simulation-based training systems in high-performance vehicle engineering.