SparrKULee: A Speech-Evoked Auditory Response Repository from KU Leuven, Containing the EEG of 85 Participants

Researchers investigating the neural mechanisms underlying speech perception often employ electroencephalography (EEG) to record brain activity while participants listen to spoken language. The high temporal resolution of EEG enables the study of neural responses to fast and dynamic speech signals....

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Bibliographic Details
Published in:Data (Basel) 2024-08, Vol.9 (8), p.94
Main Authors: Accou, Bernd, Bollens, Lies, Gillis, Marlies, Verheijen, Wendy, Van hamme, Hugo, Francart, Tom
Format: Article
Language:English
Subjects:
Audiobooks
auditory EEG
Brain
dataset
Datasets
Digital broadcasting
Electroencephalography
Encoding (Cognitive process)
Encoding-Decoding
Experiments
Learning disabilities
Machine learning
Medical research
Methods
Native languages
Podcasts
Repositories
Signal processing
Signal to noise ratio
Speech
Speech perception
Speech recognition
Spoken language
State-of-the-art reviews
Temporal resolution
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Summary:Researchers investigating the neural mechanisms underlying speech perception often employ electroencephalography (EEG) to record brain activity while participants listen to spoken language. The high temporal resolution of EEG enables the study of neural responses to fast and dynamic speech signals. Previous studies have successfully extracted speech characteristics from EEG data and, conversely, predicted EEG activity from speech features. Machine learning techniques are generally employed to construct encoding and decoding models, which necessitate a substantial quantity of data. We present SparrKULee, a Speech-evoked Auditory Repository of EEG data, measured at KU Leuven, comprising 64-channel EEG recordings from 85 young individuals with normal hearing, each of whom listened to 90–150 min of natural speech. This dataset is more extensive than any currently available dataset in terms of both the number of participants and the quantity of data per participant. It is suitable for training larger machine learning models. We evaluate the dataset using linear and state-of-the-art non-linear models in a speech encoding/decoding and match/mismatch paradigm, providing benchmark scores for future research.
ISSN:2306-5729
2306-5729
DOI:10.3390/data9080094