基于脑电信号瞬时能量的情感识别方法

Emotion Recognition Method Based on Instantaneous Energy of Electroencephalography

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%。

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 %.