Predicting Flight-Driving Attitudes through EEG-Based Models

This paper investigates the correlation between a pilot’s brain electroencephalographic (EEG) activity and his driving posture, addressing the intricate relationship between cognition and behavior in aviation. We designed and implemented a simulated experiment that recorded the fly pilot’s attitude and collected EEG information as experimental data. The experiment is only based on EEG data to predict flight posture (pull, down, left, right). We propose a flight-driving attitude prediction model (CA-FAP) based on CEBRA and self-attention mechanism, with a prediction accuracy of 0.83. This model is better than common spatial pattern (CSP) and uniform manifold approximation and projection (UMAP) dimension reduction methods in the experiment. Moreover, a better effect can be obtained in the larger attitude radian dataset (accuracy is 0.85), and the effect is not obvious in the dataset of closing the six-axis motion platform, indicating that the model prediction of flight attitude is closely related to the pilot position transformation. By comparing the prediction effect of each category, the pull-up and drop are better than the steering prediction result. The study can help pilots adjust their posture and decisions, and serve as a basis for studying flight pilot cognitive load, mental load, mood changes, and flight performance.