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A Large Finer-grained Affective Computing EEG Dataset
Affective computing based on electroencephalogram (EEG) has gained increasing attention for its objectivity in measuring emotional states. While positive emotions play a crucial role in various real-world applications, such as human-computer interactions, the state-of-the-art EEG datasets have prima...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10600242/ https://www.ncbi.nlm.nih.gov/pubmed/37880266 http://dx.doi.org/10.1038/s41597-023-02650-w |
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author | Chen, Jingjing Wang, Xiaobin Huang, Chen Hu, Xin Shen, Xinke Zhang, Dan |
author_facet | Chen, Jingjing Wang, Xiaobin Huang, Chen Hu, Xin Shen, Xinke Zhang, Dan |
author_sort | Chen, Jingjing |
collection | PubMed |
description | Affective computing based on electroencephalogram (EEG) has gained increasing attention for its objectivity in measuring emotional states. While positive emotions play a crucial role in various real-world applications, such as human-computer interactions, the state-of-the-art EEG datasets have primarily focused on negative emotions, with less consideration given to positive emotions. Meanwhile, these datasets usually have a relatively small sample size, limiting exploration of the important issue of cross-subject affective computing. The proposed Finer-grained Affective Computing EEG Dataset (FACED) aimed to address these issues by recording 32-channel EEG signals from 123 subjects. During the experiment, subjects watched 28 emotion-elicitation video clips covering nine emotion categories (amusement, inspiration, joy, tenderness; anger, fear, disgust, sadness, and neutral emotion), providing a fine-grained and balanced categorization on both the positive and negative sides of emotion. The validation results show that emotion categories can be effectively recognized based on EEG signals at both the intra-subject and the cross-subject levels. The FACED dataset is expected to contribute to developing EEG-based affective computing algorithms for real-world applications. |
format | Online Article Text |
id | pubmed-10600242 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106002422023-10-27 A Large Finer-grained Affective Computing EEG Dataset Chen, Jingjing Wang, Xiaobin Huang, Chen Hu, Xin Shen, Xinke Zhang, Dan Sci Data Data Descriptor Affective computing based on electroencephalogram (EEG) has gained increasing attention for its objectivity in measuring emotional states. While positive emotions play a crucial role in various real-world applications, such as human-computer interactions, the state-of-the-art EEG datasets have primarily focused on negative emotions, with less consideration given to positive emotions. Meanwhile, these datasets usually have a relatively small sample size, limiting exploration of the important issue of cross-subject affective computing. The proposed Finer-grained Affective Computing EEG Dataset (FACED) aimed to address these issues by recording 32-channel EEG signals from 123 subjects. During the experiment, subjects watched 28 emotion-elicitation video clips covering nine emotion categories (amusement, inspiration, joy, tenderness; anger, fear, disgust, sadness, and neutral emotion), providing a fine-grained and balanced categorization on both the positive and negative sides of emotion. The validation results show that emotion categories can be effectively recognized based on EEG signals at both the intra-subject and the cross-subject levels. The FACED dataset is expected to contribute to developing EEG-based affective computing algorithms for real-world applications. Nature Publishing Group UK 2023-10-25 /pmc/articles/PMC10600242/ /pubmed/37880266 http://dx.doi.org/10.1038/s41597-023-02650-w 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, Jingjing Wang, Xiaobin Huang, Chen Hu, Xin Shen, Xinke Zhang, Dan A Large Finer-grained Affective Computing EEG Dataset |
title | A Large Finer-grained Affective Computing EEG Dataset |
title_full | A Large Finer-grained Affective Computing EEG Dataset |
title_fullStr | A Large Finer-grained Affective Computing EEG Dataset |
title_full_unstemmed | A Large Finer-grained Affective Computing EEG Dataset |
title_short | A Large Finer-grained Affective Computing EEG Dataset |
title_sort | large finer-grained affective computing eeg dataset |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10600242/ https://www.ncbi.nlm.nih.gov/pubmed/37880266 http://dx.doi.org/10.1038/s41597-023-02650-w |
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