{"ID":2898613,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.02350","arxiv_id":"2507.02350","title":"FIRMED: A Peak-Centered Multimodal Dataset with Fine-Grained Annotation for Emotion Recognition","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.","short_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 fr...","url_abs":"https://arxiv.org/abs/2507.02350","url_pdf":"https://arxiv.org/pdf/2507.02350v3","authors":"[\"Hao Tang\",\"Songyun Xie\",\"Xinzhou Xie\",\"Can Liao\",\"Bohan Li\",\"Zhongyu Tian\",\"Dalu Zheng\"]","published":"2025-07-03T06:23:51Z","proceeding":"cs.HC","tasks":"[\"cs.HC\"]","methods":"[]","has_code":false}