基于稀疏组lasso-granger因果关系特征的脑电信号情感识别

doi:10.16451/j.cnki.issn1003-6059.201810008

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Abstract Aiming at the current feature extraction based on the functional network level of single brain region, a sparse group lasso-granger causality method is proposed to extract the causal relation among different brain regions as the characteristics of EEG at the effectual brain network level. The α, β and γ EEG bands of participants are extracted. The sparse group lasso algorithm is used to filter the obtained values of the cascade causality measures to acquire high correlation feature subsets as the emotion classification features. Finally the SVM classifier is utilized for emotion classification. Moreover, the ReliefF(filter feature selection) algorithm is employed to select an effective EEG channels to reduce the computational time complexity. The experiments show that the proposed method obtains a higher average emotion classification accuracy on the Valence-Arousal two-dimensional emotion model, and the classification result of the proposed method is better than that of the contrast EEG features. The extracted emotion EEG features can effectively recognize the subjects in different emotional states.

Received: 26 April 2018

ZTFLH:

TP 391.4

Fund:Supported by National Natural Science Foundation of China(No.61474035,61204046,61432004,61306049)

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	Articles by authors
	GUO Jinliang
	FANG Fang
	WANG Wei
	HE Hanna

Cite this article:

GUO Jinliang,FANG Fang,WANG Wei等. EEG Emotion Recognition Based on Sparse Group Lasso-Granger Causality Feature[J]. , 2018, 31(10): 941-949.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201810008 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I10/941

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