Assessment of cyclists' cognitive workload through eye tracker and EEG sensors: Sensitivity to individual and external factors in a real-world experiment

Objective: This exploratory study aims to assess cyclists' mental workload by collecting psychophysiological data, including eye-tracking and electroencephalography (EEG). A multi-factor, real-world experiment was conducted to correlate psychophysiological measures with kinematic riding features, objects in the cyclist's field of vision, and the surrounding road and urban context. Additionally, the study investigates whether subjective ratings align with objective measures. Method: Participants first completed a pre-questionnaire capturing demographic information and cycling frequency. Then, they rode an instrumented bicycle equipped with GNSS/INS sensors in real traffic conditions while wearing eye-tracking glasses and an EEG headset. This setup tracked the bicycle path and recorded gaze behaviour and brain activity. After completing the route, participants provided segment-level ratings of mental workload using the NASA-TLX questionnaire. Results: The ten participants who provided useful data indicated that cyclists could be grouped based on their recorded mental states and the visual patterns identified along the route. Due to the complexity of the correlations and the heterogeneity of the data, machine learning was applied to investigate the relevance of different features in the variability of cognitive sensor measures. The eye tracker and EEG measures revealed individual factors influencing mental workload levels and showed evidence of common and differentiated sensitivity to factors related to objects in the field of view, spatial context, and kinematic riding behaviour. Similarly, various levels of correlation were found between subjective and objective data when measuring mental workload. Conclusion: This real-world pilot study assessed mental workload using objective psychophysiological data collected via sensors, offering insights into interpreting visual patterns and EEG indicators to support the selection of the most appropriate measures for further studies. Future research can use the proposed experimental design and methodological framework to validate and extend the results to a larger population and road and traffic conditions.