Multi-Source and Multi-Representation Adaptation for Cross-Domain Electroencephalography Emotion Recognition

被引:16
作者
Cao, Jiangsheng [1 ]
He, Xueqin [1 ]
Yang, Chenhui [1 ]
Chen, Sifang [2 ]
Li, Zhangyu [2 ]
Wang, Zhanxiang [3 ,4 ]
机构
[1] Xiamen Univ, Sch Informat, Xiamen, Peoples R China
[2] Xiamen Univ, Dept Neurosurg, Affiliated Hosp 1, Xiamen, Peoples R China
[3] Xiamen Univ, Xiamen Key Lab Brain Ctr, Dept Neurosurg, Affiliated Hosp 1, Xiamen, Peoples R China
[4] Xiamen Univ, Inst Neurosurg, Sch Med, Dept Neurosci, Xiamen, Peoples R China
来源
FRONTIERS IN PSYCHOLOGY | 2022年 / 12卷
关键词
EEG; emotion recognition; domain adaption; deep learning; affective computing; SEED;
D O I
10.3389/fpsyg.2021.809459
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
Due to the non-invasiveness and high precision of electroencephalography (EEG), the combination of EEG and artificial intelligence (AI) is often used for emotion recognition. However, the internal differences in EEG data have become an obstacle to classification accuracy. To solve this problem, considering labeled data from similar nature but different domains, domain adaptation usually provides an attractive option. Most of the existing researches aggregate the EEG data from different subjects and sessions as a source domain, which ignores the assumption that the source has a certain marginal distribution. Moreover, existing methods often only align the representation distributions extracted from a single structure, and may only contain partial information. Therefore, we propose the multi-source and multi-representation adaptation (MSMRA) for cross-domain EEG emotion recognition, which divides the EEG data from different subjects and sessions into multiple domains and aligns the distribution of multiple representations extracted from a hybrid structure. Two datasets, i.e., SEED and SEED IV, are used to validate the proposed method in cross-session and cross-subject transfer scenarios, experimental results demonstrate the superior performance of our model to state-of-the-art models in most settings.
引用
收藏
页数:10
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