FIRMED: A Peak-Centered Multimodal Dataset with Fine-Grained Annotation for Emotion Recognition
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910088889892864 |
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| author | Tang, Hao Xie, Songyun Xie, Xinzhou Liao, Can Li, Bohan Tian, Zhongyu Zheng, Dalu |
| author_facet | Tang, Hao Xie, Songyun Xie, Xinzhou Liao, Can Li, Bohan Tian, Zhongyu Zheng, Dalu |
| contents | Traditional video-induced physiological datasets usually rely on whole-trial labels, which introduce temporal label noise in dynamic emotion recognition. We present FIRMED, a peak-centered multimodal dataset based on an immediate-recall annotation paradigm, with synchronized EEG, ECG, GSR, PPG, and facial recordings from 35 participants. FIRMED provides event-centered timestamps, emotion labels, and intensity annotations, and its annotation quality is supported by subjective and physiological validation. Benchmark experiments show that FIRMED consistently outperforms whole-trial labeling, yielding an average gain of 3.8 percentage points across eight EEG-based classifiers, with further improvements under multimodal fusion. FIRMED provides a practical benchmark for temporally localized supervision in multimodal affective computing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_02350 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FIRMED: A Peak-Centered Multimodal Dataset with Fine-Grained Annotation for Emotion Recognition Tang, Hao Xie, Songyun Xie, Xinzhou Liao, Can Li, Bohan Tian, Zhongyu Zheng, Dalu Human-Computer Interaction Traditional video-induced physiological datasets usually rely on whole-trial labels, which introduce temporal label noise in dynamic emotion recognition. We present FIRMED, a peak-centered multimodal dataset based on an immediate-recall annotation paradigm, with synchronized EEG, ECG, GSR, PPG, and facial recordings from 35 participants. FIRMED provides event-centered timestamps, emotion labels, and intensity annotations, and its annotation quality is supported by subjective and physiological validation. Benchmark experiments show that FIRMED consistently outperforms whole-trial labeling, yielding an average gain of 3.8 percentage points across eight EEG-based classifiers, with further improvements under multimodal fusion. FIRMED provides a practical benchmark for temporally localized supervision in multimodal affective computing. |
| title | FIRMED: A Peak-Centered Multimodal Dataset with Fine-Grained Annotation for Emotion Recognition |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2507.02350 |