LibEMER: A novel benchmark and algorithms library for EEG-based Multimodal Emotion Recognition
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909846732800000 |
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| author | Liu, Zejun Chen, Yunshan Xie, Chengxi Xie, Yugui Liu, Huan |
| author_facet | Liu, Zejun Chen, Yunshan Xie, Chengxi Xie, Yugui Liu, Huan |
| contents | EEG-based multimodal emotion recognition(EMER) has gained significant attention and witnessed notable advancements, the inherent complexity of human neural systems has motivated substantial efforts toward multimodal approaches. However, this field currently suffers from three critical limitations: (i) the absence of open-source implementations. (ii) the lack of standardized and transparent benchmarks for fair performance analysis. (iii) in-depth discussion regarding main challenges and promising research directions is a notable scarcity. To address these challenges, we introduce LibEMER, a unified evaluation framework that provides fully reproducible PyTorch implementations of curated deep learning methods alongside standardized protocols for data preprocessing, model realization, and experimental setups. This framework enables unbiased performance assessment on three widely-used public datasets across two learning tasks. The open-source library is publicly accessible at: https://anonymous.4open.science/r/2025ULUIUBUEUMUEUR485384 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19330 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LibEMER: A novel benchmark and algorithms library for EEG-based Multimodal Emotion Recognition Liu, Zejun Chen, Yunshan Xie, Chengxi Xie, Yugui Liu, Huan Signal Processing Artificial Intelligence Human-Computer Interaction Machine Learning Multimedia EEG-based multimodal emotion recognition(EMER) has gained significant attention and witnessed notable advancements, the inherent complexity of human neural systems has motivated substantial efforts toward multimodal approaches. However, this field currently suffers from three critical limitations: (i) the absence of open-source implementations. (ii) the lack of standardized and transparent benchmarks for fair performance analysis. (iii) in-depth discussion regarding main challenges and promising research directions is a notable scarcity. To address these challenges, we introduce LibEMER, a unified evaluation framework that provides fully reproducible PyTorch implementations of curated deep learning methods alongside standardized protocols for data preprocessing, model realization, and experimental setups. This framework enables unbiased performance assessment on three widely-used public datasets across two learning tasks. The open-source library is publicly accessible at: https://anonymous.4open.science/r/2025ULUIUBUEUMUEUR485384 |
| title | LibEMER: A novel benchmark and algorithms library for EEG-based Multimodal Emotion Recognition |
| topic | Signal Processing Artificial Intelligence Human-Computer Interaction Machine Learning Multimedia |
| url | https://arxiv.org/abs/2509.19330 |