MA Xin-fei,LIU Zhi-hong,JIANG Tian-hao.Study of Brain Wave Emotion Classification Algorithm[J].Journal of Chengdu University of Information Technology,2018,(04):365-369.[doi:10.16836/j.cnki.jcuit.2018.04.003]
脑电波情绪分类算法的研究
- Title:
- Study of Brain Wave Emotion Classification Algorithm
- 文章编号:
- 2096-1618(2018)04-0365-05
- Keywords:
- EEG; emotion recognition; classification accuracy; machine learning
- 分类号:
- TP391.4
- 文献标志码:
- A
- 摘要:
- 人们的情绪变化本质是大脑皮层上的高级神经活动。情绪认知应用是未来重要的一个趋势,现在以脑机接口为主流工具的研究,在脑电情绪主观世界和信号客观世界之间建立了桥梁。使用多种分类器来对情绪识别,选择有监督机器学习的Fisher、贝叶斯、SVM和无监督机器学习的DBN分类器进行研究。结果表明:在分类精度上,贝叶斯要优于Fisher,DBN要优于SVM,在运行时间上,贝叶斯运行时间最短。DBN有更高的分类精度和更低的标准偏差,平均最佳分类精度为84.01%,最低标准偏差为9.74%,比较适合脑电情绪识别。
- Abstract:
- The emotion belongs to higher nervous activity in the cerebral cortex of human. Now many researchers use BCI in formal analysis, simulation, and phototyping to explore predicted system behavior between the subjective world of emotion and the objective world of the signal. This paper compares various classifiers of emotion recognition, and then applies two sets of classifiers. The unsupervised classification include DBN, the supervised classification include Bayes classifier and Fisher classifier and SVM. The results have shown that the DNB method performed better than SVM in classification accruracy, and the Bayes classifier is better than Fisher classifier in running time. DBN has a higher classification accuracy and lower standard deviation, and it is more suitable for EEG emotion recognition. Moreover, the average classification accuracy is 84.01% and the minimum standard deviation is about9.74%.
参考文献/References:
[1] 聂聃,王晓韡,段若男,等.基于脑电的情绪识别研究综述[J].中国生物医学工程学报,2012,31(4):595-606.
[2] 刘广权.无监督自适应式脑机接口的算法研究[D].上海:上海交通大学,2010.
[3] 吴婷,颜国正,杨帮华,等.基于有监督学习的概率神经网络的脑电信号分类方法[J].上海交通大学学报,2008,42(5):803-806.
[4] 史原.基于Fisher判别法的脑电图数据分析的研究[J].微计算机信息,2010,26(7):226-228.
[5] Yang G,Lin Y,Bhattacharya P.A driver fatigue recognition model based on information fusion and dynamic Bayesian network[J].Information Sciences,2010,180(10):1942-1954.
[6] Wang Z F,Wang Z H.An Optimization Algorithm of Bayesian Network Classifiers by Derivatives of Conditional Log Likelihood[J].Chinese Journal of Computers,2012,35(2):364-374.
[7] Kummer B,Schultz R.Kuhn-Tucker-points of parametric convex programs as solutions of perturbed generalized equations[J].Revista Brasileira De Anestesiologia,1986,81:28-36.
[8] Cummer J.Methodology and Techniques for Building Modular Brain-Computer Interfaces[J].Machine Learning,2014.
[9] Guo Y,Wang S,Gao C,et al.Wishart RBM based DBN for polarimetric synthetic radar data classification[C].IEEE International Geoscience and Remote Sensing Symposium.IEEE,2015:1841-1844.
[10] 高琰,陈白帆,晁绪耀,等.基于对比散度-受限玻尔兹曼机深度学习的产品评论情感分析[J].计算机应用,2016,36(4):1045-1049.
[11] 王凯,侯著荣,王聪丽.基于交叉验证SVM的网络入侵检测[J].测试技术学报,2010,24(5):419-423.
[12] Kousarrizi M R N,Ghanbari A R A,Teshnehlab M,et al.Feature Extraction and Classification of EEG Signals Using Wavelet Transform,SVM and Artificial Neural Networks for Brain Computer Interfaces[C].International Joint Conference on Bioinformatics,Systems Biology and Intelligent Computing.IEEE,2009:352-355.
[13] Górecka J.Removing physiological artifacts from the EEG data by algorithms based on differential entropy[J].Pomiary Automatyka Kontrola,2012,58:975-977.
[14] Zheng W L,Guo H T,Lu B L.Revealing critical channels and frequency bands for emotion recognition from EEG with deep belief network[C].International Ieee/embs Conference on Neural Engineering.IEEE,2015:154-157.
[15] 刘冲,赵海滨,李春胜,等.脑电信号频带能量特征的提取方法及分类研究[J].系统仿真学报,2012,24(12):2496-2499.
备注/Memo
收稿日期:2018-03-14基金项目:四川省教育厅重点资助项目(2013ZZ0001)