Article(id=1262079399604699418, tenantId=1146029695717560320, journalId=1260987750510510108, issueId=1262079396291141802, articleNumber=null, orderNo=null, doi=10.19651/j.cnki.emt.2519375, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1752595200000, receivedDateStr=2025-07-16, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1778832777166, onlineDateStr=2026-05-15, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1778832777166, onlineIssueDateStr=2026-05-15, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1778832777166, creator=13701087609, updateTime=1778832777166, updator=13701087609, issue=Issue{id=1262079396291141802, tenantId=1146029695717560320, journalId=1260987750510510108, year='2026', volume='49', issue='6', pageStart='1', pageEnd='256', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1778832776377, creator=13701087609, updateTime=1778832914473, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1262079975688122904, tenantId=1146029695717560320, journalId=1260987750510510108, issueId=1262079396291141802, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1262079975688122905, tenantId=1146029695717560320, journalId=1260987750510510108, issueId=1262079396291141802, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=146, endPage=155, ext={EN=ArticleExt(id=1262079401701851438, articleId=1262079399604699418, tenantId=1146029695717560320, journalId=1260987750510510108, language=EN, title=Detection of pilots' cognitive states based on cross-modal physiological signal fusion, columnId=1262079401135583379, journalTitle=Electronic Measurement Technology, columnName=Data Acquisition and Signal Processing, runingTitle=null, highlight=null, articleAbstract=

Accurate assessment of pilot cognitive states is critical for ensuring flight safety, yet existing methods exhibit limitations in fusing multimodal physiological signals. To address this, this paper proposes a dual-stream deep learning network based on bidirectional cross-modal attention. The model adopts a parallel dual-branch architecture: The electroencephalography (EEG) branch quantifies brain functional connectivity through phase locking value (PLV) features and employs a densely connected network enhanced with squeeze-and-excitation (SE) modules for deep feature extraction; the electrocardiogram (ECG) branch extracts heart rate variability (HRV) and waveform features, processed by a residual-connected multilayer perceptron to characterize autonomic nervous system activity. Building upon this, an innovatively designed bidirectional cross-modal attention module dynamically weights and fuses the dual-path deep features to achieve precise classification of three states—concentrated attention, distracted attention, and startle/surprise. Experimental results on the NASA public dataset demonstrate an overall recognition accuracy of 97.44%. Ablation and comparative analyses confirm that the fusion strategy significantly outperforms single-modality analysis and simple feature concatenation methods. The study reveals that deep integration of EEG functional connectivity and ECG physiological information via attention mechanisms effectively enhances cognitive state recognition performance. This approach provides reliable technical support for developing objective and efficient pilot state monitoring systems, holding significant application value for improving flight safety.

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飞行员认知状态的精准评估对保障飞行安全至关重要，然而现有方法在多模态生理信号融合上存在局限性。为此，本文提出一种基于双向跨模态注意力的双流深度学习网络。该模型采用并行双分支架构：脑电（EEG）分支通过相位锁定值（PLV）量化大脑功能连接，并采用SE模块增强的密集连接网络进行深度特征提取；心电（ECG）分支则提取心率变异性（HRV）及波形特征，经残差连接多层感知机处理以表征自主神经系统活动。在此基础上，通过创新设计的双向跨模态注意力模块，动态加权融合双路深度特征，实现对注意力集中、分散和惊吓／惊奇3种状态的精准分类。在NASA公开数据集上的实验表明，模型总体识别准确率达97.44%。消融与对比分析证实，该融合策略显著优于单模态分析和简单的特征拼接方法。研究表明，通过注意力机制深度融合EEG功能连接与ECG生理信息，可有效提升认知状态识别性能，该方法为开发客观、高效的飞行员状态监测系统提供了可靠的技术支持，对提升飞行安全具有重要的应用价值。

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