EVA-MED: An Enhanced Valence-Arousal Multimodal Emotion Dataset for Emotion Recognition

We introduce a novel multimodal emotion recognition dataset that enhances the precision of Valence-Arousal Model while accounting for individual differences. This dataset includes electroencephalography (EEG), electrocardiography (ECG), and pulse interval (PI) from 64 participants. Data collection employed two emotion induction paradigms: video stimuli that targeted different valence levels (positive, neutral, and negative) and the Mannheim Multicomponent Stress Test (MMST), which induced high arousal through cognitive, emotional, and social stressors. To enrich the dataset, participants' personality traits, anxiety, depression, and emotional states were assessed using validated questionnaires. By capturing a broad spectrum of affective responses while accounting for individual differences, this dataset provides a robust resource for precise emotion modeling. The integration of multimodal physiological data with psychological assessments lays a strong foundation for personalized emotion recognition. We anticipate this resource will support the development of more accurate, adaptive, and individualized emotion recognition systems across diverse applications.

本研究提出一种新型多模态情绪识别数据集，该数据集在兼顾个体差异的同时，提升了效价-唤醒度模型（Valence-Arousal Model）的识别精度。该数据集收录了64名被试的脑电图（electroencephalography, EEG）、心电图（electrocardiography, ECG）以及脉搏间期（pulse interval, PI）数据。数据采集采用两种情绪诱发范式：一是针对不同效价水平（正性、中性、负性）的视频刺激范式，二是曼海姆多成分压力测试（Mannheim Multicomponent Stress Test, MMST），该测试通过认知、情绪与社会性压力源诱发高唤醒状态。为丰富数据集内容，本研究通过经过验证的问卷对被试的人格特质、焦虑水平、抑郁状态及情绪状态进行了评估。该数据集通过捕捉多维度的情感反应并兼顾个体差异，为精准情绪建模提供了可靠的研究资源。多模态生理数据与心理评估的有机结合，为个性化情绪识别研究奠定了坚实基础。本研究期望该数据集能够为跨多应用场景下更精准、自适应且个性化的情绪识别系统开发提供有力支撑。

搜集汇总

背景与挑战

背景概述

EVA-MED是一个增强版的多模态情感识别数据集，包含64名参与者的EEG、ECG和PI生理数据，以及心理评估问卷数据。该数据集通过视频刺激和压力测试两种范式诱导不同情感状态，旨在为个性化情感识别系统开发提供资源。