高技术通讯2016，Vol.26Issue(7)：617-624,8.DOI:10.3772/j.issn.1002-0470.2016.07.001
基于排列熵与多重分形指数结合的特征提取算法在情感识别中的应用
Application of the feature extraction based on combination of permutation entropy and multi-fractal index to emotion recognition
摘要
Abstract
The emotion recognition was studied by using the entropy analysis of EEG signals, and an algorithm for extrac-tion of emotion EEG features based on the combination of permutation entropy and multi fractal index was put for-ward. The algorithm achieves EEG feature extraction by combinative use of the parameters of permutation entropy, Hurst exponent, mass index and singular spectrum width, and achieves the emotion recognition by using Support Vector Machine ( SVM) . The study indicated that for one-to-one emotion recognition, the highest accuracy of the testing set was 92. 8%, all higher than 80% except for excitement against fear. The highest accuracy increased by 41. 9% compared with the permutation entropy, and 31. 2% compared with the multi-fractal index. The classifica-tion effects of positive emotion and passive emotion were further analyzed, and the average accuracy of test set was 78. 3%, respectively increased by 26. 7% and 1. 6% compared with the entropy and the multi-fractal feature. The method based on the combination of permutation entropy and multi-fractal index is proved to be an effective algo-rithm for emotion EEG feature extraction, with the capacity of sufficient obtaining the nonlinear trait and multi frac-tal feature information.关键词
脑电( EEG)信号/排列熵( PE)/多重分形指数/支持向量机( SVM)Key words
electroencephalogram (EEG) signal/permutation entropy (PE)/multi-fractal indexes/support vector machine (SVM)引用本文复制引用
李昕,齐晓英,田彦秀,孙小棋,范梦頔,蔡二娟..基于排列熵与多重分形指数结合的特征提取算法在情感识别中的应用[J].高技术通讯,2016,26(7):617-624,8.基金项目
河北省自然科学基金(F2014203244)和中国博士后科学基金(2014M550582)资助项目。 （F2014203244）
