EEG-ERnet: Emotion Recognition based on Rhythmic EEG Convolutional Neural Network Model
These findings provide valuable insights into the rhythmic characteristics of emotional EEG signals. Furthermore, the EEG-ERnet model offers a promising pathway for the development of efficient, subject-independent, and portable emotion-aware systems for real-world applications.
Authors
- Yuanyu Yu (ORCID: https://orcid.org/0000-0003-4197-0460)
- Rongjun Chen (ORCID: https://orcid.org/0000-0002-3075-460X)
- Jiujiang Wang (ORCID: https://orcid.org/0000-0001-8964-378X)
- Mang I Vai (ORCID: https://orcid.org/0000-0003-0663-0049)
- Jujian Lv (ORCID: https://orcid.org/0000-0001-7294-4172)
- Jingru Wu
- Shuang Zhang (ORCID: https://orcid.org/0000-0002-7134-0454)
- Jiawen Li (ORCID: https://orcid.org/0000-0002-8586-9535)
- Xin Liu (ORCID: https://orcid.org/0000-0002-2859-0837)
- Ling Chen (ORCID: https://orcid.org/0009-0008-6671-0204)
Institutions
- Minzu University of China (CN)
- University of Macau (MO)
- Neijiang Normal University (CN)
- Guangdong Polytechnic Normal University (CN)
- Zhuhai Institute of Advanced Technology (CN)
Publication Details
- Journal
- Journal of Integrative Neuroscience
- Published
- 2025-08-28
- DOI
- https://doi.org/10.31083/jin41547
- Citations
- 2
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 2.86
Funders
- Chengdu Medical College
- Guangdong Polytechnic Normal University
- Jilin Office of Philosophy and Social Science
- Guangzhou Municipal Science and Technology Project
- Division of Graduate Education