1. 北方工业大学信息学院
2. 华北石油通信有限公司万庄通信总站
纸质出版:2025
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李兴国, 曲洪权, 林赟, 等. 基于BLSTM-SVM混合模型的脑力负荷分类[J]. 传感器与微系统, 2025,(4):52-55.
李兴国, 曲洪权, 林赟, 等. 基于BLSTM-SVM混合模型的脑力负荷分类[J]. 传感器与微系统, 2025,(4):52-55. DOI: 10.13873/J.1000-9787(2025)04-0052-04.
在一些复杂的人机系统中,对操作人员脑力负荷的评估具有重要意义。现有的脑力负荷研究很少考虑脑电(EEG)信号切片之间的时间依赖性。因此,本文提出了一种基于双向长短期记忆—支持向量机(BLSTM-SVM)混合模型的脑力负荷分类算法。该算法将脑电信号进行z-score标准化并转换成时间序列片段,然后输入BLSTM捕获脑电信号切片间的潜在时间信息和不同脑电信号通道之间的固有时间拓扑信息。同时,考虑到脑电信号维数灾难的问题,本文算法最后使用SVM进行分类。通过与其他算法比较,BLSTM-SVM的分类正确率能达到85.29%
明显优于其他算法。所提出的算法为脑力负荷的分类研究提供了一定的参考。
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College of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China,College of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China,School of Artificial Intellgence and the Hebei Provincial Key Laboratory of Big Data Computing, Hebei University of Technology, Tianjin 300401, China,College of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China.Exploring temporal representations by leveraging attention-based bidirectional LSTM-RNNs for multi-modal emotion recognition[J].Information Processing and Management,2020.
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