ArticleNeuroImage2022
A large and rich EEG dataset for modeling human visual object recognition.
Article in NeuroImage, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Img2EEG: A Scalable and Interpretable Encoding Framework for Simulating Human EEG Responses to Visual Inputs.bioRxiv : the preprint server for biology · 2026Article
- Concept2Brain: an AI model for predicting neurophysiological responses to text and pictures.Nature communications · 2026Article
- Topographic Reorganization of EEG Complexity During Visual Mental Imagery: Insights from Lempel-Ziv Complexity in High-Density EEG.Brain topography · 2026Article
- Projection-Free CLIP-Scale EEG Latents via a U-Net-Style Autoencoder.Sensors (Basel, Switzerland) · 2026Article
- Align and Fuse: A Transformer-Based Framework for EEG-Augmented Visual Recognition.Brain sciences · 2026Article
- Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.PLoS computational biology · 2026Article
- Dynamic Representation of Multidimensional Object Properties in the Human Brain.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Increasing EEG electrode density improves decoding of visual categories and source localization: an exploratory ultra-high-density EEG study.Communications engineering · 2026Article
- Achieving more human brain-like vision via human EEG representational alignment.Communications biology · 2026Article
- A 7T fMRI dataset of synthetic images for out-of-distribution modeling of vision.Nature communications · 2026Article
- Drawings of THINGS: A large-scale drawing dataset of 1854 object concepts.Behavior research methods · 2026Article
- Decoding visual object recognition from EEG signals.PloS one · 2026Article
- Article
- Teaching CORnet human fMRI representations for enhanced model-brain alignment.Cognitive neurodynamics · 2025Article
- The effects of task similarity during representation learning in brains and neural networks.Nature communications · 2025Article
- Article
- In silico discovery of representational relationships across visual cortex.Nature human behaviour · 2025Article
- Task-relevant representational spaces in human memory traces.PLoS computational biology · 2025Article
- Unfolding spatiotemporal representations of 3D visual perception in the human brain.bioRxiv : the preprint server for biology · 2025Article
- Human EEG and artificial neural networks reveal disentangled representations and processing timelines of object real-world size and depth in natural images.bioRxiv : the preprint server for biology · 2025Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The human brain achieves visual object recognition through multiple stages of linear and nonlinear transformations operating at a millisecond scale. To predict and explain these rapid transformations, computational neuroscientists employ machine learning modeling techniques. However, state-of-the-art models require massive amounts of data to properly train, and to the present day there is a lack of vast brain datasets which extensively sample the temporal dynamics of visual object recognition. Here we collected a large and rich dataset of high temporal resolution EEG responses to images of objects on a natural background. This dataset includes 10 participants, each with 82,160 trials spanning 16,740 image conditions. Through computational modeling we established the quality of this dataset in five ways. First, we trained linearizing encoding models that successfully synthesized the EEG responses to arbitrary images. Second, we correctly identified the recorded EEG data image conditions in a zero-shot fashion, using EEG synthesized responses to hundreds of thousands of candidate image conditions. Third, we show that both the high number of conditions as well as the trial repetitions of the EEG dataset contribute to the trained models' prediction accuracy. Fourth, we built encoding models whose predictions well generalize to novel participants. Fifth, we demonstrate full end-to-end training of randomly initialized DNNs that output EEG responses for arbitrary input images. We release this dataset as a tool to foster research in visual neuroscience and computer vision.
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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 OpenQuestion graph.