An fMRI dataset in response to large-scale short natural dynamic facial expression videos.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-11-19 DOI:10.1038/s41597-024-04088-0
Panpan Chen, Chi Zhang, Bao Li, Li Tong, LinYuan Wang, ShuXiao Ma, Long Cao, ZiYa Yu, Bin Yan
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Abstract

Facial expression is among the most natural methods for human beings to convey their emotional information in daily life. Although the neural mechanisms of facial expression have been extensively studied employing lab-controlled images and a small number of lab-controlled video stimuli, how the human brain processes natural dynamic facial expression videos still needs to be investigated. To our knowledge, this type of data specifically on large-scale natural facial expression videos is currently missing. We describe here the natural Facial Expressions Dataset (NFED), an fMRI dataset including responses to 1,320 short (3-second) natural facial expression video clips. These video clips are annotated with three types of labels: emotion, gender, and ethnicity, along with accompanying metadata. We validate that the dataset has good quality within and across participants and, notably, can capture temporal and spatial stimuli features. NFED provides researchers with fMRI data for understanding of the visual processing of large number of natural facial expression videos.

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针对大规模自然动态面部表情短视频的 fMRI 数据集。
面部表情是人类在日常生活中传递情感信息最自然的方法之一。尽管人们已经利用实验室控制的图像和少量实验室控制的视频刺激对面部表情的神经机制进行了广泛研究,但人脑如何处理自然动态面部表情视频仍有待研究。据我们所知,目前还没有专门针对大规模自然面部表情视频的此类数据。我们在此描述了自然面部表情数据集(NFED),这是一个 fMRI 数据集,包括对 1,320 个短小(3 秒)的自然面部表情视频片段的反应。这些视频片段标注了三种类型的标签:情绪、性别和种族,以及相应的元数据。我们验证了该数据集在参与者内部和参与者之间都具有良好的质量,特别是可以捕捉时间和空间刺激特征。NFED 为研究人员了解大量自然面部表情视频的视觉处理提供了 fMRI 数据。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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