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- Title
Multi-Source and Multi-Representation Adaptation for Cross-Domain Electroencephalography Emotion Recognition.
- Authors
Cao, Jiangsheng; He, Xueqin; Yang, Chenhui; Chen, Sifang; Li, Zhangyu; Wang, Zhanxiang
- Abstract
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.
- Subjects
EMOTION recognition; ELECTROENCEPHALOGRAPHY; MARGINAL distributions; AFFECTIVE computing; ARTIFICIAL intelligence
- Publication
Frontiers in Psychology, 2022, Vol 12, p1
- ISSN
1664-1078
- Publication type
Article
- DOI
10.3389/fpsyg.2021.809459