交通信息与安全2025，Vol.43Issue(6)：117-127,11.DOI:10.3963/j.jssn.1674-4861.2025.06.012
基于脑电特征信号的民航飞行学员工作负荷识别方法
An EEG-based Workload Recognition Method for Civil Aviation Student Pilots
摘要
Abstract
The workload of civil aviation student pilots directly impacts flight safety.To address the limitations of ex-isting electroencephalogram(EEG)based pilot workload recognition methods,such as poor model generalization and insufficient utilization of cross-band and spatial features.This study investigates and develops an EEG-based ap-proach for workload recognition in civil aviation student pilots.An integrated subjective-objective evaluation frame-work is established.EEG signals and NASA Task Load Index(NASA-TLX)data are collected from student pilots under different task scenarios in a simulated flight environment to concurrently acquire both objective physiological measurements and subjective workload assessments.To overcome the limitation of traditional studies that isolate in-dividual frequency bands and neglect inter-band interactions,an independent samples t-test is applied to identify EEG features with significant differences(P<0.05).Furthermore,by incorporating whole-brain power spectral den-sity activation maps,the neural response mechanisms,and spatial distribution patterns of the θ,δ,α,and β bands,as well as cross-band power ratios,are analyzed under varying workload levels.Third,the extracted EEG fea-tures from the full frequency band and each sub-frequency band are used for model training,and a hybrid model based on convolutional neural network(CNN)and long short-term memory(LSTM)for workload recognition is de-veloped to achieve accurate recognition of workload states.Experimental results showed that the selected features could distinguish the neural regulation modes under different workloads.At high workload,the spectral power of the α,θ,and β bands increased in civil aviation student pilots,while the spectral power of the δ band decreased.Specifically,the θ rhythm facilitated the priority allocation of resources through a frontal-parietal and right tempo-ral circuit,while α rhythms exhibited enhanced interference suppression along the left temporal-parietal-prefrontal pathway.The constructed model successfully captured both the spatial and temporal dynamics of EEG signals.Moreover,the hybrid model achieved a test accuracy of 94.5%,outperforming traditional single models such as CNN,LSTM,and Transformer.Notably,using α-band features alone yielded a test accuracy of 95.5%,confirming the efficacy of the proposed method in identifying pilot workload states.关键词
飞行安全/工作负荷/脑电信号/机器学习/特征分析Key words
flight safety/workload/EEG signal/machine learning/feature analysis分类
航空航天引用本文复制引用
刘凌波,司海青,尚磊,汪海波,李天昊,李小俊..基于脑电特征信号的民航飞行学员工作负荷识别方法[J].交通信息与安全,2025,43(6):117-127,11.基金项目
国家自然科学基金民航联合基金重点项目(U2033202)、江苏省研究生科研与实践创新项目(KYCX250636)、航空科学基金项目(2024Z071052007)、中央高校基本科研业务费资助(NJ2024029)、南京航空航天大学"实验技术研究与开发"项目(SYJS202207Y)资助 （U2033202）
