Multi-Modal Domain Adaptation Variational Autoencoder for EEG-Based Emotion Recognition

Traditional electroencephalograph (EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject, which restricts the application of the affective brain computer interface (BCI) in practice. We attempt to use the multi-modal data from the past...

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Vydané v:	IEEE/CAA journal of automatica sinica Ročník 9; číslo 9; s. 1612 - 1626
Hlavní autori:	Wang, Yixin, Qiu, Shuang, Li, Dan, Du, Changde, Lu, Bao-Liang, He, Huiguang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway Chinese Association of Automation (CAA) 01.09.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptation models Brain modeling Calibration Cycle-consistency Data models domain adaptation Domains electroencephalograph (EEG) Electroencephalography Emotion recognition Emotions Human-computer interface Image reconstruction Knowledge management Modal data multi modality Regularization Representations variational autoencoder
ISSN:	2329-9266, 2329-9274
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Abstract	Traditional electroencephalograph (EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject, which restricts the application of the affective brain computer interface (BCI) in practice. We attempt to use the multi-modal data from the past session to realize emotion recognition in the case of a small amount of calibration samples. To solve this problem, we propose a multi-modal domain adaptive variational autoencoder (MMDA-VAE) method, which learns shared cross-domain latent representations of the multi-modal data. Our method builds a multi-modal variational autoencoder (MVAE) to project the data of multiple modalities into a common space. Through adversarial learning and cycle-consistency regularization, our method can reduce the distribution difference of each domain on the shared latent representation layer and realize the transfer of knowledge. Extensive experiments are conducted on two public datasets, SEED and SEED-IV, and the results show the superiority of our proposed method. Our work can effectively improve the performance of emotion recognition with a small amount of labelled multi-modal data.
AbstractList	Traditional electroencephalograph (EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject, which restricts the application of the affective brain computer interface (BCI) in practice. We attempt to use the multi-modal data from the past session to realize emotion recognition in the case of a small amount of calibration samples. To solve this problem, we propose a multi-modal domain adaptive variational autoencoder (MMDA-VAE) method, which learns shared cross-domain latent representations of the multi-modal data. Our method builds a multi-modal variational autoencoder (MVAE) to project the data of multiple modalities into a common space. Through adversarial learning and cycle-consistency regularization, our method can reduce the distribution difference of each domain on the shared latent representation layer and realize the transfer of knowledge. Extensive experiments are conducted on two public datasets, SEED and SEED-IV, and the results show the superiority of our proposed method. Our work can effectively improve the performance of emotion recognition with a small amount of labelled multi-modal data.
Author	Li, Dan He, Huiguang Qiu, Shuang Wang, Yixin Lu, Bao-Liang Du, Changde
Author_xml	– sequence: 1 givenname: Yixin surname: Wang fullname: Wang, Yixin email: wangyxai@hotmail.com organization: Research Center for Brain-inspired Intelligence, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science,Beijing,100190 – sequence: 2 givenname: Shuang surname: Qiu fullname: Qiu, Shuang email: shuang.qiu@ia.ac.cn organization: Research Center for Brain-inspired Intelligence, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science,Beijing,100190 – sequence: 3 givenname: Dan surname: Li fullname: Li, Dan email: danliai@hotmail.com organization: Research Center for Brain-inspired Intelligence, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science,Beijing,100190 – sequence: 4 givenname: Changde surname: Du fullname: Du, Changde email: duchangde@gmail.com organization: Research Center for Brain-inspired Intelligence, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science,Beijing,100190 – sequence: 5 givenname: Bao-Liang surname: Lu fullname: Lu, Bao-Liang email: bllu@sjtu.edu.cn organization: Shanghai Jiao Tong University,Department of Computer Science and Engineering,Shanghai,China,200240 – sequence: 6 givenname: Huiguang surname: He fullname: He, Huiguang email: huiguang.he@ia.ac.cn organization: Research Center for Brain-inspired Intelligence, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science,Beijing,100190
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SubjectTerms	Adaptation models Brain modeling Calibration Cycle-consistency Data models domain adaptation Domains electroencephalograph (EEG) Electroencephalography Emotion recognition Emotions Human-computer interface Image reconstruction Knowledge management Modal data multi modality Regularization Representations variational autoencoder
Title	Multi-Modal Domain Adaptation Variational Autoencoder for EEG-Based Emotion Recognition
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