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  Human EEG recordings for 1,854 concepts presented in rapid serial visual presentation streams

Grootswagers, T., Zhou, I., Robinson, A. K., Hebart, M. N., & Carlson, T. A. (2022). Human EEG recordings for 1,854 concepts presented in rapid serial visual presentation streams. Scientific Data, 9(1): 3. doi:10.1038/s41597-021-01102-7.

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 Urheber:
Grootswagers, Tijl1, 2, Autor
Zhou, Ivy2, Autor
Robinson, Amanda K.2, Autor
Hebart, Martin N.3, Autor           
Carlson, Thomas A.2, Autor
Affiliations:
1The MARCS Institute, University of Western Sydney, Australia, ou_persistent22              
2School of Psychology, University of Sydney, Australia, ou_persistent22              
3Max Planck Research Group Vision and Computational Cognition, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_3158378              

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Schlagwörter: Perception; Sensory processing
 Zusammenfassung: 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.

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Sprache(n): eng - English
 Datum: 2021-07-262021-11-032022-01-10
 Publikationsstatus: Online veröffentlicht
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 Art der Begutachtung: -
 Identifikatoren: DOI: 10.1038/s41597-021-01102-7
PMID: 35013331
PMC: PMC8748587
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Grant ID : ARC DP160101300 (TAC); ARC DP200101787 (TAC); ARC. DE200101159
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Förderorganisation : ARC

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Titel: Scientific Data
  Kurztitel : Sci. Data
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: London, United Kingdom : Nature Publishing Group
Seiten: - Band / Heft: 9 (1) Artikelnummer: 3 Start- / Endseite: - Identifikator: ISSN: 2052-4463
CoNE: https://pure.mpg.de/cone/journals/resource/2052-4463