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arXiv · 2507.02350

FIRMED: A Peak-Centered Multimodal Dataset with Fine-Grained Annotation for Emotion Recognition

Hao Tang·Songyun Xie·Xinzhou Xie·Can Liao·Bohan Li·Zhongyu Tian·Dalu Zheng
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Abstract

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.

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Hao Tang, Songyun Xie, Xinzhou Xie, Can Liao, Bohan Li, Zhongyu Tian, Dalu Zheng. 2025-07-03. FIRMED: A Peak-Centered Multimodal Dataset with Fine-Grained Annotation for Emotion Recognition. https://arxiv.org/abs/2507.02350

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