While affective computing has advanced considerably, multimodal emotion prediction in aging populations remains underexplored, largely due to the scarcity of dedicated datasets. Existing multimodal benchmarks predominantly target young, cognitively healthy subjects, neglecting the influence of cognitive decline on emotional expression and physiological responses. To bridge this gap, we present MECO, a Multimodal dataset for Emotion and Cognitive understanding in Older adults. MECO includes 42 participants and provides approximately 38 hours of multimodal signals, yielding 30,592 synchronized samples. To maximize ecological validity, data collection followed standardized protocols within community-based settings. The modalities cover video, audio, electroencephalography (EEG), and electrocardiography (ECG). In addition, the dataset offers comprehensive annotations of emotional and cognitive states, including self-assessed valence, arousal, six basic emotions, and Mini-Mental State Examination cognitive scores. We further establish baseline benchmarks for both emotion and cognitive prediction. MECO serves as a foundational resource for multimodal modeling of affect and cognition in aging populations, facilitating downstream applications such as personalized emotion recognition and early detection of mild cognitive impairment (MCI) in real-world settings. The complete dataset and supplementary materials are available at https://maitrechen.github.io/meco-page/.

翻译：尽管情感计算已取得显著进展，针对老年群体的多模态情感预测仍研究不足，主要受限于专用数据集的匮乏。现有多模态基准数据集主要面向年轻、认知健康的受试者，忽视了认知衰退对情感表达和生理反应的影响。为弥补这一空白，我们提出MECO——面向老年人情感与认知理解的多模态数据集。该数据集包含42名参与者，约38小时的多模态信号，共生成30,592个同步样本。为最大程度提高生态效度，数据采集遵循标准化方案并在社区环境中进行。模态涵盖视频、音频、脑电图（EEG）和心电图（ECG）。此外，数据集还提供了情感与认知状态的全面标注，包括自评效价、唤醒度、六种基本情绪以及简明精神状态检查认知评分。我们进一步建立了情感与认知预测的基线基准。MECO作为老年群体情感与认知多模态建模的基础资源，可推动个性化情感识别及真实场景下轻度认知障碍（MCI）早期检测等下游应用。完整数据集及补充材料见https://maitrechen.github.io/meco-page/。