ArticleScientific data2022
Human EEG recordings for 1,854 concepts presented in rapid serial visual presentation streams.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
34 citing papers in PubMed.
- Decoding object categories from electroencephalography during free viewing reveals early information evolution compared to passive viewing.Journal of vision · 2026Article
- The neural processes of illusory occlusion in object recognition.Journal of vision · 2026Article
- Disentangling objects' contextual associations from perceptual and conceptual attributes using time-resolved neural decoding.Journal of vision · 2026Article
- Concept2Brain: an AI model for predicting neurophysiological responses to text and pictures.Nature communications · 2026Article
- Dynamic Representation of Multidimensional Object Properties in the Human Brain.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- How Robust Are fMRI- and EEG-Based Representational Similarity Analysis?Computational brain & behavior · 2026Article
- A comprehensive EEG dataset for investigating visual touch perception.Scientific data · 2026Article
- Artificial intelligence as a surrogate brain: bridging neural dynamical models and data.National science review · 2026Review
- Drawings of THINGS: A large-scale drawing dataset of 1854 object concepts.Behavior research methods · 2026Article
- CNeuroMod-THINGS, a densely-sampled fMRI dataset for visual neuroscience.Scientific data · 2026Article
- Robust neural decoding with low-density EEG.Scientific reports · 2026Article
- Article
- Towards decoding individual words from non-invasive brain recordings.Nature communications · 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
- How EEG preprocessing shapes decoding performance.Communications biology · 2025Article
- A multi-subject and multi-session EEG dataset for modelling human visual object recognition.Scientific data · 2025Article
- Human infant EEG recordings for 200 object images presented in rapid visual streams.Scientific data · 2025Article
- Mapping object space dimensions: New insights from temporal dynamics.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Rapid visual engagement in neural processing of detailed touch interactions.Imaging neuroscience (Cambridge, Mass.) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The neural basis of object recognition and semantic knowledge has been extensively studied but the high dimensionality of object space makes it challenging to develop overarching theories on how the brain organises object knowledge. To help understand how the brain allows us to recognise, categorise, and represent objects and object categories, there is a growing interest in using large-scale image databases for neuroimaging experiments. In the current paper, we present THINGS-EEG, a dataset containing human electroencephalography responses from 50 subjects to 1,854 object concepts and 22,248 images in the THINGS stimulus set, a manually curated and high-quality image database that was specifically designed for studying human vision. The THINGS-EEG dataset provides neuroimaging recordings to a systematic collection of objects and concepts and can therefore support a wide array of research to understand visual object processing in the human brain.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.