分段复合多尺度模糊熵和IGWO-SVM的脑电情感识别
脑电情感识别在辅助测谎、临床医学、人机交互中发挥着重要作用。为提高脑电的情感识别率,提出了分段复合多尺度模糊熵算法,采用分段粗粒化和计算复合多尺度模糊熵的策略,使提取的特征较好的解决了数据缺失和计算不准确的问题;同时构造了应用余弦非线性收敛因子和动静态位置更新的灰狼算法优化支持向量机的分类模型。为证明所提的两种算法的有效性,进行了仿真实验的验证,并在公开DEAP数据库下与几种常见的支持向量机优化模型比较脑电的情感识别率,结果表明在提出的模型下,效价、唤醒度、优势度、喜欢度的平均识别率分别为87.27%、87.81%、89.06%、87.58%,均高于其他算法。另外对比了高/低喜欢度下效价和唤醒度的分类,实验说明喜欢度低时情感识别率较高。
EEG(Electroencephalography) emotion recognition are important in auxiliary lie detection, clinical medicine and human-computer interaction. The paper used a piecewise complex multi-scale fuzzy entropy to increase the recognition rate. It proposed piecewise coarse graining and calculating complex multi-scale fuzzy entropy. It could solve the problem of missing data and calculating inaccurate entropy. This paper constructed an IGWO-SVM classification model which used cosine nonlinear convergence factor and dynamic and static position. To prove the validity of the algorithms, The paper performed the simulation experiment. Comparing the EEG emotion recognition rate with several common SVM optimization models in the public DEAP database, the results show that the average recognition rates of valance, arousal, dominance and liking are 87.27%, 87.81%, 89.06% and 87.58% in proposed model, which is higher than other algorithms. Comparing the classification of valance and arousal under high/low liking, experiments show that the recognition rate is higher when the liking is lower.
魏雪、吴清
医学研究方法基础医学神经病学、精神病学
脑电信号情感识别改进灰狼优化算法SVM优化算法分段复合多尺度模糊熵
魏雪,吴清.分段复合多尺度模糊熵和IGWO-SVM的脑电情感识别[EB/OL].(2018-08-13)[2025-08-25].https://chinaxiv.org/abs/201808.00088.点此复制
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