EEG-based Emotion Style Transfer Network for Cross-dataset Emotion Recognition Article Swipe
Yijin Zhou
,
Fu Li
,
Yang Li
,
Youshuo Ji
,
Lijian Zhang
,
Yuanfang Chen
,
Wenming Zheng
,
Guangming Shi
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.05767
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.05767
As the key to realizing aBCIs, EEG emotion recognition has been widely studied by many researchers. Previous methods have performed well for intra-subject EEG emotion recognition. However, the style mismatch between source domain (training data) and target domain (test data) EEG samples caused by huge inter-domain differences is still a critical problem for EEG emotion recognition. To solve the problem of cross-dataset EEG emotion recognition, in this paper, we propose an EEG-based Emotion Style Transfer Network (E2STN) to obtain EEG representations that contain the content information of source domain and the style information of target domain, which is called stylized emotional EEG representations. The representations are helpful for cross-dataset discriminative prediction. Concretely, E2STN consists of three modules, i.e., transfer module, transfer evaluation module, and discriminative prediction module. The transfer module encodes the domain-specific information of source and target domains and then re-constructs the source domain's emotional pattern and the target domain's statistical characteristics into the new stylized EEG representations. In this process, the transfer evaluation module is adopted to constrain the generated representations that can more precisely fuse two kinds of complementary information from source and target domains and avoid distorting. Finally, the generated stylized EEG representations are fed into the discriminative prediction module for final classification. Extensive experiments show that the E2STN can achieve the state-of-the-art performance on cross-dataset EEG emotion recognition tasks.
Related Topics
Concepts
Discriminative model
Computer science
Electroencephalography
Stylized fact
Artificial intelligence
Transfer of learning
Pattern recognition (psychology)
Domain (mathematical analysis)
Speech recognition
Emotion classification
Psychology
Mathematics
Economics
Macroeconomics
Mathematical analysis
Psychiatry
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2308.05767
- https://arxiv.org/pdf/2308.05767
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385825289
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385825289Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2308.05767Digital Object Identifier
- Title
-
EEG-based Emotion Style Transfer Network for Cross-dataset Emotion RecognitionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-09Full publication date if available
- Authors
-
Yijin Zhou, Fu Li, Yang Li, Youshuo Ji, Lijian Zhang, Yuanfang Chen, Wenming Zheng, Guangming ShiList of authors in order
- Landing page
-
https://arxiv.org/abs/2308.05767Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2308.05767Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2308.05767Direct OA link when available
- Concepts
-
Discriminative model, Computer science, Electroencephalography, Stylized fact, Artificial intelligence, Transfer of learning, Pattern recognition (psychology), Domain (mathematical analysis), Speech recognition, Emotion classification, Psychology, Mathematics, Economics, Macroeconomics, Mathematical analysis, PsychiatryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.domains | 138, 187 |
| abstract_inverted_index.emotion | 7, 24, 54, 63, 221 |
| abstract_inverted_index.encodes | 130 |
| abstract_inverted_index.helpful | 106 |
| abstract_inverted_index.methods | 17 |
| abstract_inverted_index.module, | 119, 122 |
| abstract_inverted_index.module. | 126 |
| abstract_inverted_index.pattern | 146 |
| abstract_inverted_index.problem | 51, 59 |
| abstract_inverted_index.propose | 69 |
| abstract_inverted_index.samples | 41 |
| abstract_inverted_index.studied | 12 |
| abstract_inverted_index.Finally, | 191 |
| abstract_inverted_index.However, | 26 |
| abstract_inverted_index.Previous | 16 |
| abstract_inverted_index.Transfer | 74 |
| abstract_inverted_index.consists | 113 |
| abstract_inverted_index.critical | 50 |
| abstract_inverted_index.domain's | 144, 150 |
| abstract_inverted_index.mismatch | 29 |
| abstract_inverted_index.modules, | 116 |
| abstract_inverted_index.process, | 161 |
| abstract_inverted_index.stylized | 99, 156, 194 |
| abstract_inverted_index.transfer | 118, 120, 128, 163 |
| abstract_inverted_index.(training | 33 |
| abstract_inverted_index.EEG-based | 71 |
| abstract_inverted_index.Extensive | 207 |
| abstract_inverted_index.constrain | 169 |
| abstract_inverted_index.emotional | 100, 145 |
| abstract_inverted_index.generated | 171, 193 |
| abstract_inverted_index.performed | 19 |
| abstract_inverted_index.precisely | 176 |
| abstract_inverted_index.realizing | 4 |
| abstract_inverted_index.evaluation | 121, 164 |
| abstract_inverted_index.prediction | 125, 202 |
| abstract_inverted_index.Concretely, | 111 |
| abstract_inverted_index.differences | 46 |
| abstract_inverted_index.distorting. | 190 |
| abstract_inverted_index.experiments | 208 |
| abstract_inverted_index.information | 85, 92, 133, 182 |
| abstract_inverted_index.performance | 217 |
| abstract_inverted_index.prediction. | 110 |
| abstract_inverted_index.recognition | 8, 222 |
| abstract_inverted_index.statistical | 151 |
| abstract_inverted_index.inter-domain | 45 |
| abstract_inverted_index.recognition, | 64 |
| abstract_inverted_index.recognition. | 25, 55 |
| abstract_inverted_index.researchers. | 15 |
| abstract_inverted_index.complementary | 181 |
| abstract_inverted_index.cross-dataset | 61, 108, 219 |
| abstract_inverted_index.intra-subject | 22 |
| abstract_inverted_index.re-constructs | 141 |
| abstract_inverted_index.discriminative | 109, 124, 201 |
| abstract_inverted_index.characteristics | 152 |
| abstract_inverted_index.classification. | 206 |
| abstract_inverted_index.domain-specific | 132 |
| abstract_inverted_index.representations | 80, 104, 172, 196 |
| abstract_inverted_index.representations. | 102, 158 |
| abstract_inverted_index.state-of-the-art | 216 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.7300000190734863 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile |