Abstract:

The landing task is characterized by time pressure and a complex operational process,making it crucial to accurately assess pilots' mental fatigue to enhance landing safety.To address the issue of evaluating pilots' mental fatigue during landing tasks,a series of simulated landing experiments of varying difficulties are conducted,with EEG signals from nine participants over a nine-day period collected.A cross-subject mental fatigue assessment model based on SSENet is developed for the landing scenario.To capture spatial information coupling and channel feature information in the EEG signals,an SEConv module is designed within the model,targeting the spatial characteristics of the EEG signals and cross-subject training methods.The results show significant differences in the level of mental fatigue among participants during the various difficulty levels of landing tasks(p<0.001).The model achieves a maximum classification accuracy of 95.55% during five-fold cross-validation,with an average classification accuracy of 93.00%.Ablation experiments verify the effectiveness of each module,with a classification accuracy improvement of approximately 4% compared to the classical EEG signal training model EEGNet.The SSENet demonstrates promising results in the cross-subject mental fatigue assessment task,offering new strategies for enhancing the research on landing safety.

Key words: electroencephalography, mental fatigue, landing, pilot, deep learning