Variational Autoencoder based Latent Factor Decoding of Multichannel EEG for Emotion Recognition

被引:0
|
作者
Li, Xiang [1 ]
Zhao, Zhigang [1 ]
Song, Dawei [2 ]
Zhang, Yazhou [3 ]
Niu, Chunyang [1 ]
Zhang, Junwei [4 ]
Huo, Jidong [1 ]
Li, Jing [5 ]
机构
[1] Qilu Univ Technol, Shandong Acad Sci, Natl Supercomp Ctr Jinan, Key Lab Med Artificial Intelligence,Shandong Comp, Jinan, Peoples R China
[2] Beijing Inst Technol, Sch Comp Sci & Technol, Beijing, Peoples R China
[3] Zhengzhou Univ Light Ind, Softwarc Engn Coll, Zhengzhou, Peoples R China
[4] Tianjin Univ, Sch Comp Sci & Technol, Tianjin, Peoples R China
[5] Jiuquan Satellite Launch Ctr, Alxa League, Peoples R China
来源
2019 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) | 2019年
关键词
Affective Computing; Latent Factor Decoding; Emotion Recognition; EEG; Variational Autoencoder;
D O I
暂无
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Robust cross-subject emotion recognition based on multichannel EEG has always been a hard work. In this work, we hypothesize there exists default brain variables across subjects in emotional processes. Hence, the states of the latent variables that related to emotional processing must contribute to building robust recognition models. We propose to utilize variational autoencoder (VAE) to determine the latent factors from the multi-channel EEG. Through sequence modeling method, we examine the emotion recognition performance based on the learnt latent factors. The performance of the proposed methodology is verified on two public datasets (DEAP and SEED), and compared with traditional matrix factorization based (ICA) and autoencoder based (AE) approaches. Experimental results demonstrate that neural network is suitable for unsupervised EEG modeling and our proposed emotion recognition framework achieves the state-of-the-art performance. As far as we know, it is the first work that introduces VAE into multichannel EEG decoding for emotion recognition.
引用
收藏
页码:684 / 687
页数:4
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