用于研究动态视觉事件处理的人类脑电图响应大型数据集——针对短自然视频
A large dataset of human EEG responses to short naturalistic videos for studying dynamic visual event processing
- Freie Universität Berlin（柏林自由大学）
- Charité – Universitätsmedizin Berlin（柏林夏里特医科大学）
- Humboldt-Universität zu Berlin（柏林洪堡大学）
- University of Amsterdam（阿姆斯特丹大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文推出EEG Moments Dataset（EMD），含6名受试者观看1102段短自然视频的EEG与眼动数据，结合BMD可开展动态视觉事件大脑响应的时空解析研究，并公开数据及教程供使用。
AI中文摘要：
视觉神经科学领域中，针对自然图像的大脑响应大型数据集的收集与应用已大幅增长。然而静态图像缺乏理解大脑在动态现实场景中如何处理视觉信息所必需的时间维度。为推动动态视觉事件感知的神经关联研究，本文推出脑电图时刻数据集（EEG Moments Dataset, EMD）。EMD包含6名受试者观看1102段短自然视频（时长3秒，带音频）并保持中央注视时采集的128通道脑电图（EEG）响应及眼动追踪记录。研究表明，EMD的EEG响应可良好编码刺激相关信息，与视频刺激存在时间对应关系，且基于不同特征空间的大脑编码模型揭示其具有丰富的表征内容。此外，结合同批视频对应的现有人类功能磁共振成像（fMRI）大型数据集——BOLD时刻数据集（BMD），EMD可实现对动态视觉事件大脑响应的时空解析研究。本文以原始和预处理格式发布EMD的EEG与眼动追踪数据、1102段视频刺激及丰富的刺激元数据，还提供交互式代码教程，帮助用户熟悉EMD的预处理数据、刺激及刺激元数据。
英文摘要：
Vision neuroscience has experienced a surge in the collection and use of large-scale datasets of brain responses to naturalistic images. However, static images lack the temporal dimension essential for understanding how vision is solved in the brain during dynamic real life settings. To facilitate the study of the neural correlates of dynamic visual event perception, we introduce the EEG Moments Dataset (EMD). EMD consists of 128-channel EEG responses and eye-tracking recordings of 6 human participants viewing 1,102 short naturalistic videos (3-second long; with audio track) while maintaining central fixation. We show that EMD's EEG responses well encode stimulus-related information, exhibit a temporal correspondence with the video stimuli, and have a rich representational content revealed by brain encoding models based on different feature spaces. Furthermore, complemented by the BOLD Moments Dataset (BMD) - an existing large-scale dataset of human functional magnetic resonance imaging (fMRI) responses for the same videos - EMD enables spatio-temporally resolved investigations of brain responses to dynamic visual events. We release EMD's EEG and eye-tracking data in both raw and preprocessed format, along with the 1,102 video stimuli, and rich stimulus metadata. Finally, we provide an interactive code tutorial to familiarize with EMD's preprocessed data, stimuli, and stimulus metadata.
