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An innovative EEG-based emotion recognition using a single channel-specific feature from the brain rhythm code method

INTRODUCTION: Efficiently recognizing emotions is a critical pursuit in brain–computer interface (BCI), as it has many applications for intelligent healthcare services. In this work, an innovative approach inspired by the genetic code in bioinformatics, which utilizes brain rhythm code features cons...

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Autores principales: Li, Jia Wen, Lin, Di, Che, Yan, Lv, Ju Jian, Chen, Rong Jun, Wang, Lei Jun, Zeng, Xian Xian, Ren, Jin Chang, Zhao, Hui Min, Lu, Xu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10397731/
https://www.ncbi.nlm.nih.gov/pubmed/37547144
http://dx.doi.org/10.3389/fnins.2023.1221512
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author Li, Jia Wen
Lin, Di
Che, Yan
Lv, Ju Jian
Chen, Rong Jun
Wang, Lei Jun
Zeng, Xian Xian
Ren, Jin Chang
Zhao, Hui Min
Lu, Xu
author_facet Li, Jia Wen
Lin, Di
Che, Yan
Lv, Ju Jian
Chen, Rong Jun
Wang, Lei Jun
Zeng, Xian Xian
Ren, Jin Chang
Zhao, Hui Min
Lu, Xu
author_sort Li, Jia Wen
collection PubMed
description INTRODUCTION: Efficiently recognizing emotions is a critical pursuit in brain–computer interface (BCI), as it has many applications for intelligent healthcare services. In this work, an innovative approach inspired by the genetic code in bioinformatics, which utilizes brain rhythm code features consisting of δ, θ, α, β, or γ, is proposed for electroencephalography (EEG)-based emotion recognition. METHODS: These features are first extracted from the sequencing technique. After evaluating them using four conventional machine learning classifiers, an optimal channel-specific feature that produces the highest accuracy in each emotional case is identified, so emotion recognition through minimal data is realized. By doing so, the complexity of emotion recognition can be significantly reduced, making it more achievable for practical hardware setups. RESULTS: The best classification accuracies achieved for the DEAP and MAHNOB datasets range from 83–92%, and for the SEED dataset, it is 78%. The experimental results are impressive, considering the minimal data employed. Further investigation of the optimal features shows that their representative channels are primarily on the frontal region, and associated rhythmic characteristics are typical of multiple kinds. Additionally, individual differences are found, as the optimal feature varies with subjects. DISCUSSION: Compared to previous studies, this work provides insights into designing portable devices, as only one electrode is appropriate to generate satisfactory performances. Consequently, it would advance the understanding of brain rhythms, which offers an innovative solution for classifying EEG signals in diverse BCI applications, including emotion recognition.
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spelling pubmed-103977312023-08-04 An innovative EEG-based emotion recognition using a single channel-specific feature from the brain rhythm code method Li, Jia Wen Lin, Di Che, Yan Lv, Ju Jian Chen, Rong Jun Wang, Lei Jun Zeng, Xian Xian Ren, Jin Chang Zhao, Hui Min Lu, Xu Front Neurosci Neuroscience INTRODUCTION: Efficiently recognizing emotions is a critical pursuit in brain–computer interface (BCI), as it has many applications for intelligent healthcare services. In this work, an innovative approach inspired by the genetic code in bioinformatics, which utilizes brain rhythm code features consisting of δ, θ, α, β, or γ, is proposed for electroencephalography (EEG)-based emotion recognition. METHODS: These features are first extracted from the sequencing technique. After evaluating them using four conventional machine learning classifiers, an optimal channel-specific feature that produces the highest accuracy in each emotional case is identified, so emotion recognition through minimal data is realized. By doing so, the complexity of emotion recognition can be significantly reduced, making it more achievable for practical hardware setups. RESULTS: The best classification accuracies achieved for the DEAP and MAHNOB datasets range from 83–92%, and for the SEED dataset, it is 78%. The experimental results are impressive, considering the minimal data employed. Further investigation of the optimal features shows that their representative channels are primarily on the frontal region, and associated rhythmic characteristics are typical of multiple kinds. Additionally, individual differences are found, as the optimal feature varies with subjects. DISCUSSION: Compared to previous studies, this work provides insights into designing portable devices, as only one electrode is appropriate to generate satisfactory performances. Consequently, it would advance the understanding of brain rhythms, which offers an innovative solution for classifying EEG signals in diverse BCI applications, including emotion recognition. Frontiers Media S.A. 2023-07-20 /pmc/articles/PMC10397731/ /pubmed/37547144 http://dx.doi.org/10.3389/fnins.2023.1221512 Text en Copyright © 2023 Li, Lin, Che, Lv, Chen, Wang, Zeng, Ren, Zhao and Lu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Li, Jia Wen
Lin, Di
Che, Yan
Lv, Ju Jian
Chen, Rong Jun
Wang, Lei Jun
Zeng, Xian Xian
Ren, Jin Chang
Zhao, Hui Min
Lu, Xu
An innovative EEG-based emotion recognition using a single channel-specific feature from the brain rhythm code method
title An innovative EEG-based emotion recognition using a single channel-specific feature from the brain rhythm code method
title_full An innovative EEG-based emotion recognition using a single channel-specific feature from the brain rhythm code method
title_fullStr An innovative EEG-based emotion recognition using a single channel-specific feature from the brain rhythm code method
title_full_unstemmed An innovative EEG-based emotion recognition using a single channel-specific feature from the brain rhythm code method
title_short An innovative EEG-based emotion recognition using a single channel-specific feature from the brain rhythm code method
title_sort innovative eeg-based emotion recognition using a single channel-specific feature from the brain rhythm code method
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10397731/
https://www.ncbi.nlm.nih.gov/pubmed/37547144
http://dx.doi.org/10.3389/fnins.2023.1221512
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