一种基于样本补偿与混合模型的特种设备操作人员脑电疲劳检测方法

By combining multidimensional feature extraction and sample compensation with a hybrid model of deep learning and traditional machine learning, the problems of insufficient feature extraction, data imbalance and computational complexity in EEG fatigue detection of special equipment operators are solved, and high-precision, real-time fatigue detection is achieved.

CN121570176BActive Publication Date: 2026-07-17SHENYANG UNIVERSITY OF TECHNOLOGY +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
SHENYANG UNIVERSITY OF TECHNOLOGY
Filing Date
2025-12-01
Publication Date
2026-07-17

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Abstract

本发明公开了一种基于样本补偿与混合模型的特种设备操作人员脑电疲劳检测方法，包括：采集特种设备操作人员的脑电信号并进行预处理，得到预处理后的脑电信号；对预处理后的脑电信号进行多域特征提取和融合，获得多维特征集；采用样本补偿方法对脑电信号和多维特征集进行补偿处理，得到补偿后的样本特征集；其中，所述样本补偿方法包括特征级补偿方法和信号级补偿方法；通过补偿后的样本特征集对混合模型进行训练，得到训练后的混合模型；其中，所述混合模型包括深度学习模型和传统机器学习模型；通过训练后的混合模型对特种设备操作人员进行脑电疲劳检测，得到的检测结果。
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