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

Tijl Grootswagers; Ivy Zhou; Amanda K. Robinson; Martin N. Hebart; Thomas A. Carlson

doi:10.1038/s41597-021-01102-7

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

Tijl Grootswagers, Ivy Zhou, Amanda K. Robinson, Martin N. Hebart, Thomas A. Carlson

Western Sydney University

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Number of pages	7
Journal	Scientific Data
DOIs	https://doi.org/10.1038/s41597-021-01102-7
Publication status	Published - 2022

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

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.",

author = "Tijl Grootswagers and Ivy Zhou and Robinson, {Amanda K.} and Hebart, {Martin N.} and Carlson, {Thomas A.}",

year = "2022",

doi = "10.1038/s41597-021-01102-7",

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PY - 2022

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

Abstract

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