BEAM: Brainwave Empathy Assessment Model for Early Childhood
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915484229697536 |
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| author | Xie, Chen Wu, Gaofeng Wang, Kaidong Zhu, Zihao Luo, Xiaoshu Liang, Yan Quan, Feiyu Wu, Ruoxi Huang, Xianghui Zhang, Han |
| author_facet | Xie, Chen Wu, Gaofeng Wang, Kaidong Zhu, Zihao Luo, Xiaoshu Liang, Yan Quan, Feiyu Wu, Ruoxi Huang, Xianghui Zhang, Han |
| contents | Empathy in young children is crucial for their social and emotional development, yet predicting it remains challenging. Traditional methods often only rely on self-reports or observer-based labeling, which are susceptible to bias and fail to objectively capture the process of empathy formation. EEG offers an objective alternative; however, current approaches primarily extract static patterns, neglecting temporal dynamics. To overcome these limitations, we propose a novel deep learning framework, the Brainwave Empathy Assessment Model (BEAM), to predict empathy levels in children aged 4-6 years. BEAM leverages multi-view EEG signals to capture both cognitive and emotional dimensions of empathy. The framework comprises three key components: 1) a LaBraM-based encoder for effective spatio-temporal feature extraction, 2) a feature fusion module to integrate complementary information from multi-view signals, and 3) a contrastive learning module to enhance class separation. Validated on the CBCP dataset, BEAM outperforms state-of-the-art methods across multiple metrics, demonstrating its potential for objective empathy assessment and providing a preliminary insight into early interventions in children's prosocial development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_06620 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BEAM: Brainwave Empathy Assessment Model for Early Childhood Xie, Chen Wu, Gaofeng Wang, Kaidong Zhu, Zihao Luo, Xiaoshu Liang, Yan Quan, Feiyu Wu, Ruoxi Huang, Xianghui Zhang, Han Machine Learning Artificial Intelligence Empathy in young children is crucial for their social and emotional development, yet predicting it remains challenging. Traditional methods often only rely on self-reports or observer-based labeling, which are susceptible to bias and fail to objectively capture the process of empathy formation. EEG offers an objective alternative; however, current approaches primarily extract static patterns, neglecting temporal dynamics. To overcome these limitations, we propose a novel deep learning framework, the Brainwave Empathy Assessment Model (BEAM), to predict empathy levels in children aged 4-6 years. BEAM leverages multi-view EEG signals to capture both cognitive and emotional dimensions of empathy. The framework comprises three key components: 1) a LaBraM-based encoder for effective spatio-temporal feature extraction, 2) a feature fusion module to integrate complementary information from multi-view signals, and 3) a contrastive learning module to enhance class separation. Validated on the CBCP dataset, BEAM outperforms state-of-the-art methods across multiple metrics, demonstrating its potential for objective empathy assessment and providing a preliminary insight into early interventions in children's prosocial development. |
| title | BEAM: Brainwave Empathy Assessment Model for Early Childhood |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.06620 |