特征融合实现脑电信号情感分析

doi:10.19665/j.issn1001-2400.2022.06.012

Abstract

Abstract:

Since the EEG signal has the characteristics of non-stationary,weak,and large frequency difference,it is difficult to obtain a higher recognition accuracy.In order to improve the performance of EEG sentiment analysis,this paper conducts research from two aspects:feature extraction and feature selection.In terms of feature extraction,based on the power spectrum intensity,the balanced power spectrum intensity feature (BPSI) is adopted.For feature selection,a feature fusion algorithm FFS is proposed,which combines the Relief and mRMR to reduce the feature dimension and improve the recognition performance.This paper uses the SVM classification algorithm,and carries out experiments on DEAP data.Experimental results show that,compared with the power spectrum intensity,the classification accuracy of the BPSI feature is increased by 6.22% on average.The performance is increased by 3.9 points with features selected by the FFS compared with the baseline,by 1.84 points compared with the Relief,and by 2.05 points compared with the mRMR.The average accuracy of the emotion recognition algorithm based on the BPSI and FFS reaches 88.89% in Valence dimension and 87.73% in Arousal dimension,and meanwhile the average feature dimension is reduced from 160 to 67.

Key words: emotion recognition, affective computing, electro encephalo gram signal, feature extraction, feature fusion, feature selection

CLC Number:

TP181

YANG Liying,MENG Tianhao,ZHANG Qingyang,CHAO Si. Implementation of EEG emotion analysis via feature fusion[J].Journal of Xidian University, 2022, 49(6): 95-102.

Figures/Tables 4

References 24

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项目	描述
受试者	32(16位男士,16位女士)
通道数	40(32个脑电信号,8个其他生理信号)
影片长度	3 s(准备时间),60 s(刺激时间)
标签类型	Valence、Arousal、Dominance、Liking
采样频率/Hz	128

特征	Valence维度	Arousal维度
ESD	74.52	70.75
PSD	74.88	70.64
RIR	85.64	85.06
DE	87.72	87.38
PSI	82.78	80.99
BPSI	88.14	88.07

方法	Valence维度	Arousal维度
mRMR^[14] (2016)	69.90	71.20
NMI^[15](2017)	76.80	74.30
庞加莱交叉点^[16](2019)	87.42	84.59
两部投票 ^[17] (2020)	85.00	86.30
文中算法	88.89	87.73

Implementation of EEG emotion analysis via feature fusion

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