航空科学技术2024，Vol.35Issue(11)：95-103,9.DOI:10.19452/j.issn1007-5453.2024.11.012
基于脑电信号的飞行员认知负荷实时监测评估系统
Real-time Mental Workload Monitoring and Evaluation System Based on EEG Signals of Pilots
李葳宁 1韩宗昌 1邢晨光1
作者信息
- 1. 中国航空系统工程研究所,北京 100012
- 折叠
摘要
Abstract
Real-time monitoring and assessment of pilots'cognitive load status when performing air combat missions play an important role in ensuring the safety and efficiency of missions.Based on EEG signals,this paper proposes a DGCN-LSTM cognitive load assessment model.This method extracts the spatial topological features of EEG based on dynamic graph convolution networks,and fuses the temporal information of features at different locations in the time dimension through the LSTM network.Finally,the spatial and temporal features are extracted and fused to evaluate the cognitive workload status via the fully connected layers as a classifier.In order to verify the feasibility of the algorithm,the experimental paradigm simulated a variety of typical air combat missions by establishing a flight mission simulation platform,setting up mission scenarios with different complexities to stimulate pilots'different levels of cognitive load states,and collecting the subjects'EEG signals for model training and evaluation.In the sample data set collected in this article's experiment,the average accuracy of this algorithm in cognitive load classification reaches 89.08%,with 1.24M parameters of model,and its performance is better than other algorithm networks based on traditional machine learning,CNN,RNN,and GCN backbone.This work is proved to realize real-time mental workload estimation of pilots accurately.关键词
认知负荷评估/脑电信号分析/图神经网络/LSTM/实时监测系统Key words
mental workload estimation/EEG signals analysis/graph neural networks/LSTM/real-time monitoring system分类
信息技术与安全科学引用本文复制引用
李葳宁,韩宗昌,邢晨光..基于脑电信号的飞行员认知负荷实时监测评估系统[J].航空科学技术,2024,35(11):95-103,9.
