西安电子科技大学学报（自然科学版）2025，Vol.52Issue(2)：33-46,14.DOI:10.19665/j.issn1001-2400.20250204
基于SSENet的飞行员脑力疲劳评估方法
Pilot mental fatigue assessment method based on the SSENet
摘要
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.关键词
脑电信号/脑力疲劳/飞行员/着舰/深度学习Key words
electroencephalography/mental fatigue/landing/pilot/deep learning引用本文复制引用
金恒,孙有朝,曾一宁,刘威成,郭媛媛..基于SSENet的飞行员脑力疲劳评估方法[J].西安电子科技大学学报（自然科学版）,2025,52(2):33-46,14.基金项目
国家自然科学基金委员会-中国民用航空局联合基金(U2033202,U1333119) （U2033202,U1333119）
国家自然科学基金(52172387) （52172387）
中央高校基本科研业务费专项资金(ILA22032-1A) （ILA22032-1A）
中国航空科学基金(2022Z071052001) （2022Z071052001）
南京航空航天大学研究生科研与实践创新计划(xcxjh20230728) （xcxjh20230728）
河南省高校人文社会科学研究一般项目(2025-ZZJH-017) （2025-ZZJH-017）
