基于脑电特征信号的民航飞行学员工作负荷识别方法OACSCD

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.