基于脑电信号瞬时能量的情感识别方法

doi:10.19678/j.issn.1000-3428.0050486

计算机工程 ›› 2019, Vol. 45 ›› Issue (4): 196-204. doi: 10.19678/j.issn.1000-3428.0050486

基于脑电信号瞬时能量的情感识别方法

陈田^1a,1b,陈占刚^1a,1b,袁晓辉²,鞠思航^1a,1b,任福继^1a,1b,3

1.合肥工业大学 a.计算机与信息学院; b.情感计算与先进智能机器安徽省重点实验室,合肥 230009; 2.北德克萨斯州大学计算机与工程学院,美国丹顿市 76203; 3.德岛大学工学部,日本德岛 770-8506

收稿日期:2018-02-11 出版日期:2019-04-15 发布日期:2019-04-15
作者简介:陈田(1974—),女,副教授,主研方向为情感计算;陈占刚,硕士研究生;袁晓辉,副教授;鞠思航,硕士研究生;任福继,教授。
基金资助:
国家自然科学基金重点项目(61432004);国家自然科学基金(61204046,61474035);国家留学基金(201706695016)。

Emotion Recognition Method Based on Instantaneous Energy of Electroencephalography

CHEN Tian^1a,1b,CHEN Zhan’gang^1a,1b ,YUAN Xiaohui²,JU Sihang^1a,1b,REN Fuji^1a,1b,3

1a.School of Computer and Information;1b.Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine,Hefei University of Technology,Hefei 230009,China; 2.School of Computer and Engineering,University of North Texas,Denton 76203,USA; 3.Faculty of Engineering,The University of Tokushima,Tokushima 770-8506,Japan

Received:2018-02-11 Online:2019-04-15 Published:2019-04-15

摘要/Abstract

摘要：

希尔伯特-黄变换(HHT)是一种处理脑电信号(EEG)的有效方法,包括经验模态分解(EMD)和Hilbert变换2个部分。但EMD无法分解包含低能量的信号,且在低频区域会产生不良的本征模态函数。为消除EMD的弊端,提出一种小波包变换(WPT)和HHT相结合的EEG处理方法。采用WPT将EEG分解成一组窄带信号,通过HHT得到Hilbert能量谱,求出平均瞬时能量作为EEG特征并封装成特征矩阵。将特征矩阵通过卷积神经网络(CNN)、递归神经网络(RNN)、支持向量机(SVM)组成的混合情感识别模型进行训练与分类。实验结果表明,该方法对高兴、悲伤、平静、恐惧4种情感的平均识别率为86.22%,最优识别率为93.45%。

关键词: 脑电信号, 情感识别, 希尔伯特-黄变换, 卷积神经网络, 递归神经网络

Abstract:

Hilbert-Huang Transform(HHT) is an effective method to deal with Electroencephalography(EEG) that includes two parts:Empirical Mode Decomposition(EMD) and Hilbert transform.However,the EMD cannot decompose a signal of low energy and will produce bad Intrinsic Mode Functions(IMF) in the low frequency region.To eliminate the effects of EMD,this paper proposes an EEG processing method which combines Wavelet Packet Transform(WPT) and HHT.Firstly,the EEG is decomposed into a set of narrow-band signals by WPT,the Hilbert energy spectrum of EEG is obtained by HHT,and the average value of the instantaneous energies is calculated as the EEG feature and packaged into a feature matrix.The feature matrix is trained and classified by mixed emotion recognition model which consists of Convolutional Neural Network(CNN),Recurrent Neural Network(RNN),and Support Vector Machine(SVM).Experimental results show that the average recognition rate and the best recognition rate of the four emotions which are happiness,sadness,calmness,and fear are 86.22% and 93.45%.

Key words: Electroencephalography(EEG), emotion recognition, Hilbert-Huang Transform(HHT), Convolutional Neural Network(CNN), Recurrent Neural Network(RNN)

中图分类号:

TP391

陈田,陈占刚,袁晓辉,鞠思航,任福继. 基于脑电信号瞬时能量的情感识别方法[J]. 计算机工程, 2019, 45(4): 196-204.

CHEN Tian,CHEN Zhan’gang,YUAN Xiaohui,JU Sihang,REN Fuji. Emotion Recognition Method Based on Instantaneous Energy of Electroencephalography[J]. Computer Engineering, 2019, 45(4): 196-204.

http://www.ecice06.com/CN/Y2019/V45/I4/196

参考文献

［1］CHU S,NARAYANAN S,KUO C C J.Environmental sound recognition using MP-based features［C］//Proceedings of IEEE International Conference on Acoustics,Speech and Signal Processing.Washington D.C.,USA:IEEE Press,2008:1-4.
［2］CANNON W B.The James-Lange theory of emotions:a critical examination and an alternative theory［J］.The American Journal of Psychology,1987,100(3/4):567-586.
［3］陆文娟.基于脑电信号的情感识别研究［D］.南京:南京邮电大学,2017.
［4］BAJAJ V,PACHORI R B.Detection of human emotions using features based on the multiwavelet transform of EEG signals［M］.Berlin,Germany:Springer,2015:215-240.
［5］ARNAU-GONZLEZ P,AREVALILLO-HERREZ M,RAMZAN N.Fusing highly dimensional energy and connectivity features to identify affective states from EEG signals［J］.Neurocomputing,2017,244:81-89.
［6］LIN Y P,WANG C H,JUNG T P.EEG-based emotion recognition in music listening［J］.IEEE Transactions on Biomedical Engineering,2010,57(7):1798-1806.
［7］KARLSSON L,LOUTFI A.Sleep stage classification using unsupervised feature learning［J］.Advances in Artificial Neural Systems,2012.
［8］LI K,LI X,ZHANG Y.Affective state recognition from EEG with deep belief networks［C］//Proceedings of IEEE International Conference on Bioinformatics and Biomedicine.Washington D.C.,USA:IEEE Press,2013:305-310.
［9］吴琛,周瑞忠.Hilbert-Huang变换在提取地震信号动力特性中的应用［J］.地震工程与工程振动,2006,26(5):41-46.
［10］PETRANTONAKIS P C,HADJILEONTIADIS L J.A novel emotion elicitation index using frontal brain asymmetry for enhanced EEG-based emotion recognition［J］.IEEE Transactions on Information Technology in Biomedicine,2011,15(5):737-46.
［11］XU Y L,CHEN J.Characterizing nonstationary wind speed using empirical mode decomposition［J］.Journal of Structural Engineering,2004,130(6):912-920.
［12］PENG Z K,TSE P W,CHU F L.An improved Hilbert-Huang transform and its application in vibration signal analysis［J］.Journal of Sound and Vibration,2005,286(1/2):187-205.
［13］赵双乐.浅析小波变换理论及其应用［J］.科技风,2014(15):106-106.
［14］石瑞敏,杨兆建.基于改进EMD的多绳摩擦提升机载荷信息特征提取［J］.煤炭学报,2014,39(4):782-788.
［15］BADSHAH A M,AHMAD J,RAHIM N,et al.Speech emotion recognition from spectrograms with deep convolutional neural network［C］//Proceedings of International Conference on Platform Technology and Service.Washington D.C.,USA:IEEE Press,2017:1-5.
［16］BENGIO Y,SIMARD P,FRASCONI P.Learning long-term dependencies with gradient descent is difficult［J］.IEEE Transactions on Neural Networks,1994,5(2):157-166.
［17］李幼军,黄佳进,王海渊.基于SAE和LSTM RNN的多模态生理信号融合和情感识别研究［J］.通信学报,2017,38(12):109-120.
［18］CORTES C,VAPNIK V.Support-vector networks［J］.Machine Learning,1995,20(3):273-297.
［19］NIE D,WANG X W,SHI L C,et al.EEG-based emotion recognition during watching movies［C］//Proceedings of International IEEE/EMBS Conference on Neural Engineering.Washington D.C.,USA:IEEE Press,2011:667-670.
［20］PAUL S,MAZUMDER A,GHOSH P,et al.EEG based emotion recognition system using MFDFA as feature extractor［C］//Proceedings of International Conference on Robotics,Automation,Control and Embedded Systems.Washington D.C.,USA:IEEE Press,2015:1-5.
［21］ZHENG W L,LU B L.Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks［J］.IEEE Transactions on Autonomous Mental Development,2015,7(3):162-175.
［22］LIU S,MENG J,ZHANG D,et al.Emotion recognition based on EEG changes in movie viewing［C］//Proceedings of International IEEE/EMBS Conference on Neural Engineering.Washington D.C.,USA:IEEE Press,2015:1036-1039.
［23］AFTANAS L I,REVA N V,VARLAMOV A A,et al.Analysis of evoked EEG synchronization and desynchronization in conditions of emotional activation in humans:temporal and topographic characteristics［J］.Neuroscience and Behavioral Physiology,2004,34(8):859-67.
［24］JENKE R,PEER A,BUSS M.Feature extraction and selection for emotion recognition from EEG［J］.IEEE Transactions on Affective Computing,2017,5(3):327-339.

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