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.
Highlights
Background & SummaryHumans are able to visually recognise and meaningfully interact with a large number of different objects, despite drastic changes in retinal projection, lighting or viewing angle, and the objects being positioned in cluttered visual environments
Recent work has created large, curated image sets that are designed for studying the cognitive and neural basis of human vision[4,6]. One of these is THINGS4, which is an image set containing 1,854 object concepts representing a comprehensive set of nameable concepts used in the English language, accompanied by 26,107 associated manually-curated high-quality image exemplars and human behavioural annotations
The phase is to collate corresponding neural responses to stimuli in THINGS. This would contribute to an emerging landscape of large datasets of neural responses to curated image sets that accelerate research in visual, computational, and cognitive neuroscience[8]
Summary
Humans are able to visually recognise and meaningfully interact with a large number of different objects, despite drastic changes in retinal projection, lighting or viewing angle, and the objects being positioned in cluttered visual environments. Recent work has pointed out concept-level differences in mean luminance between images in the THINGS image set[16], which can be controlled for in future analyses of the THINGS-EEG dataset. Another point to consider is that our recording setup did not include EOG or EMG channels, which means the dataset does not contain external recordings of eye or other muscle movements. As THINGS is a high-quality stimulus set of record size, THINGS-EEG accompanies this resource with a comprehensive set of human neuroimaging recordings
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