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Maritime fire incidents pose a significant threat to the safety of ships, with human factors being the primary cause of these accidents. Accurately identifying the emotional changes of crew members in maritime fire scenarios is of great significance for enhancing their firefighting capabilities. Virtual reality technology is employed to simulate maritime fire scenes and collect Electroencephalogram (EEG) signals from multiple subjects. The EEG signals are preprocessed and decomposed into sub-signals of different frequency bands using discrete wavelet transform. Three features, including mean absolute value, standard deviation, and root mean square, are extracted from each sub-frequency band to establish a feature set. Multiple machine learning models suitable for emotion recognition are constructed, and the models are evaluated using metrics such as precision, accuracy, and F₁ score. Experimental results show that the support vector machine classification model performs the best, with an accuracy of 87.97%, which significantly improves the three-class classification problem of crew members' fear emotions in maritime environments. Combining virtual reality technology with EEG emotion recognition techniques can effectively induce and identify crew members' fear emotions in fire scenarios. This method is beneficial for assessing and improving the emergency response capabilities of crew members in firefighting training.

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船舶火灾事故对船舶安全构成重大威胁，人为因素是事故发生的主要因素。准确识别船员在船舶火灾场景下的恐惧情绪变化，对提升船员的火灾处置能力有重要意义。采用虚拟现实技术模拟海上火灾场景收集多个受试者的脑电信号。对脑电信号进行预处理以及离散小波变换将原始信号分解为不同频段的子信号，提取每个子频段的3种特征建立特征集，包括平均绝对值、标准差和均方根。构建多个情绪识别领域适用的机器学习模型，采用精确率、准确率和F₁等评估指标对模型进行评估。试验结果表明：支持向量机分类模型效果最好，准确率达到了87.97%，在船员的恐惧情绪三分类问题中效果显著。将虚拟现实技术与脑电信号情绪识别技术相结合，可有效地诱发和识别船员在火灾场景下的恐惧情绪。该方法有助于在船员消防培训中，评估和提升船员的应急能力。

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孙铭泽（1999—），男，硕士生，研究方向为船舶仿真建模。E-mail:18004087635@163.com

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孙铭泽（1999—），男，硕士生，研究方向为船舶仿真建模。E-mail:18004087635@163.com

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