FIRMED：一种以峰值为中心的多模态数据集，用于情感识别的细粒度标注
FIRMED: A Peak-Centered Multimodal Dataset with Fine-Grained Annotation for Emotion Recognition
AI总结：
FIRMED通过即时回忆标注方法，提供同步的EEG、ECG、GSR、PPG和面部记录，以峰值为中心的标注减少了动态情感识别中的时间标签噪声，实验表明其在八种EEG分类器上平均提升3.8个百分点。
摘要：
传统视频诱导生理数据集通常依赖于整段标注，这在动态情感识别中引入了时间标签噪声。我们提出了FIRMED，一种基于即时回忆标注方法的峰值中心多模态数据集，包含35名参与者的同步EEG、ECG、GSR、PPG和面部记录。FIRMED提供了事件中心时间戳、情感标签和强度标注，其标注质量通过主观和生理验证支持。基准实验表明，FIRMED在八种EEG分类器上平均提升3.8个百分点，多模态融合下进一步提升。FIRMED为多模态情感计算中的时间局部监督提供了实用基准。
英文摘要：
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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