ArticleScientific data2023
A large-scale fMRI dataset for human action recognition.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning.Scientific data · 2026Article
- Bottom-up and generative computations uniquely explain neural responses across the social brain.bioRxiv : the preprint server for biology · 2026Article
- Distinct information flows for different whole-body actions across LOTC subdivisions.Communications biology · 2025Article
- A large-scale MEG and EEG dataset for object recognition in naturalistic scenes.Scientific data · 2025Article
- Principles of intensive human neuroimaging.Trends in neurosciences · 2024Article
- Modeling short visual events through the BOLD moments video fMRI dataset and metadata.Nature communications · 2024Article
- A large-scale fMRI dataset for human action recognition.Scientific data · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
Human action recognition is a critical capability for our survival, allowing us to interact easily with the environment and others in everyday life. Although the neural basis of action recognition has been widely studied using a few action categories from simple contexts as stimuli, how the human brain recognizes diverse human actions in real-world environments still needs to be explored. Here, we present the Human Action Dataset (HAD), a large-scale functional magnetic resonance imaging (fMRI) dataset for human action recognition. HAD contains fMRI responses to 21,600 video clips from 30 participants. The video clips encompass 180 human action categories and offer a comprehensive coverage of complex activities in daily life. We demonstrate that the data are reliable within and across participants and, notably, capture rich representation information of the observed human actions. This extensive dataset, with its vast number of action categories and exemplars, has the potential to deepen our understanding of human action recognition in natural environments.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.