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A 10-hour within-participant magnetoencephalography narrative dataset to test models of language comprehension.

Authors :
Armeni K
Güçlü U
van Gerven M
Schoffelen JM
Source :
Scientific data [Sci Data] 2022 Jun 08; Vol. 9 (1), pp. 278. Date of Electronic Publication: 2022 Jun 08.
Publication Year :
2022

Abstract

Recently, cognitive neuroscientists have increasingly studied the brain responses to narratives. At the same time, we are witnessing exciting developments in natural language processing where large-scale neural network models can be used to instantiate cognitive hypotheses in narrative processing. Yet, they learn from text alone and we lack ways of incorporating biological constraints during training. To mitigate this gap, we provide a narrative comprehension magnetoencephalography (MEG) data resource that can be used to train neural network models directly on brain data. We recorded from 3 participants, 10 separate recording hour-long sessions each, while they listened to audiobooks in English. After story listening, participants answered short questions about their experience. To minimize head movement, the participants wore MEG-compatible head casts, which immobilized their head position during recording. We report a basic evoked-response analysis showing that the responses accurately localize to primary auditory areas. The responses are robust and conserved across 10 sessions for every participant. We also provide usage notes and briefly outline possible future uses of the resource.<br /> (© 2022. The Author(s).)

Details

Language :
English
ISSN :
2052-4463
Volume :
9
Issue :
1
Database :
MEDLINE
Journal :
Scientific data
Publication Type :
Academic Journal
Accession number :
35676293
Full Text :
https://doi.org/10.1038/s41597-022-01382-7