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Open access dataset integrating EEG and fNIRS during Stroop tasks

Conflict monitoring and processing are crucial components of the human cognitive system, with significant implications for daily life and the diagnosis of cognitive disorders. The Stroop task, combined with brain function detection technology, has been widely employed as a classical paradigm for inv...

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Autores principales: Chen, Zemeng, Gao, Chenyang, Li, Ting, Ji, Xiang, Liu, Shuyu, Xiao, Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10497617/
https://www.ncbi.nlm.nih.gov/pubmed/37699935
http://dx.doi.org/10.1038/s41597-023-02524-1
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author Chen, Zemeng
Gao, Chenyang
Li, Ting
Ji, Xiang
Liu, Shuyu
Xiao, Ming
author_facet Chen, Zemeng
Gao, Chenyang
Li, Ting
Ji, Xiang
Liu, Shuyu
Xiao, Ming
author_sort Chen, Zemeng
collection PubMed
description Conflict monitoring and processing are crucial components of the human cognitive system, with significant implications for daily life and the diagnosis of cognitive disorders. The Stroop task, combined with brain function detection technology, has been widely employed as a classical paradigm for investigating conflict processing. However, there remains a lack of public datasets that integrate Electroencephalogram (EEG) and functional Near-infrared Spectroscopy (fNIRS) to simultaneously record brain activity during a Stroop task. We introduce a dual-modality Stroop task dataset incorporating 34-channel EEG (sampling frequency is 1000 Hz) and 20-channel high temporal resolution fNIRS (sampling frequency is 100 Hz) measurements covering the whole frontal cerebral cortex from 21 participants (9 females/12 males, aged 23.0 ± 2.3 years). Event-related potential analysis of EEG recordings and activation analysis of fNIRS recordings were performed to show the significant Stroop effect. We expected that the data provided would be utilized to investigate multimodal data processing algorithms during cognitive processing.
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spelling pubmed-104976172023-09-14 Open access dataset integrating EEG and fNIRS during Stroop tasks Chen, Zemeng Gao, Chenyang Li, Ting Ji, Xiang Liu, Shuyu Xiao, Ming Sci Data Data Descriptor Conflict monitoring and processing are crucial components of the human cognitive system, with significant implications for daily life and the diagnosis of cognitive disorders. The Stroop task, combined with brain function detection technology, has been widely employed as a classical paradigm for investigating conflict processing. However, there remains a lack of public datasets that integrate Electroencephalogram (EEG) and functional Near-infrared Spectroscopy (fNIRS) to simultaneously record brain activity during a Stroop task. We introduce a dual-modality Stroop task dataset incorporating 34-channel EEG (sampling frequency is 1000 Hz) and 20-channel high temporal resolution fNIRS (sampling frequency is 100 Hz) measurements covering the whole frontal cerebral cortex from 21 participants (9 females/12 males, aged 23.0 ± 2.3 years). Event-related potential analysis of EEG recordings and activation analysis of fNIRS recordings were performed to show the significant Stroop effect. We expected that the data provided would be utilized to investigate multimodal data processing algorithms during cognitive processing. Nature Publishing Group UK 2023-09-12 /pmc/articles/PMC10497617/ /pubmed/37699935 http://dx.doi.org/10.1038/s41597-023-02524-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Chen, Zemeng
Gao, Chenyang
Li, Ting
Ji, Xiang
Liu, Shuyu
Xiao, Ming
Open access dataset integrating EEG and fNIRS during Stroop tasks
title Open access dataset integrating EEG and fNIRS during Stroop tasks
title_full Open access dataset integrating EEG and fNIRS during Stroop tasks
title_fullStr Open access dataset integrating EEG and fNIRS during Stroop tasks
title_full_unstemmed Open access dataset integrating EEG and fNIRS during Stroop tasks
title_short Open access dataset integrating EEG and fNIRS during Stroop tasks
title_sort open access dataset integrating eeg and fnirs during stroop tasks
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10497617/
https://www.ncbi.nlm.nih.gov/pubmed/37699935
http://dx.doi.org/10.1038/s41597-023-02524-1
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