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EEG-Based Emotion Recognition with Consideration of Individual Difference

Electroencephalograms (EEGs) are often used for emotion recognition through a trained EEG-to-emotion models. The training samples are EEG signals recorded while participants receive external induction labeled as various emotions. Individual differences such as emotion degree and time response exist...

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Detalles Bibliográficos
Autores principales: Xia, Yuxiao, Liu, Yinhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10535213/
https://www.ncbi.nlm.nih.gov/pubmed/37765808
http://dx.doi.org/10.3390/s23187749
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author Xia, Yuxiao
Liu, Yinhua
author_facet Xia, Yuxiao
Liu, Yinhua
author_sort Xia, Yuxiao
collection PubMed
description Electroencephalograms (EEGs) are often used for emotion recognition through a trained EEG-to-emotion models. The training samples are EEG signals recorded while participants receive external induction labeled as various emotions. Individual differences such as emotion degree and time response exist under the same external emotional inductions. These differences can lead to a decrease in the accuracy of emotion classification models in practical applications. The brain-based emotion recognition model proposed in this paper is able to sufficiently consider these individual differences. The proposed model comprises an emotion classification module and an individual difference module (IDM). The emotion classification module captures the spatial and temporal features of the EEG data, while the IDM introduces personalized adjustments to specific emotional features by accounting for participant-specific variations as a form of interference. This approach aims to enhance the classification performance of EEG-based emotion recognition for diverse participants. The results of our comparative experiments indicate that the proposed method obtains a maximum accuracy of 96.43% for binary classification on DEAP data. Furthermore, it performs better in scenarios with significant individual differences, where it reaches a maximum accuracy of 98.92%.
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spelling pubmed-105352132023-09-29 EEG-Based Emotion Recognition with Consideration of Individual Difference Xia, Yuxiao Liu, Yinhua Sensors (Basel) Article Electroencephalograms (EEGs) are often used for emotion recognition through a trained EEG-to-emotion models. The training samples are EEG signals recorded while participants receive external induction labeled as various emotions. Individual differences such as emotion degree and time response exist under the same external emotional inductions. These differences can lead to a decrease in the accuracy of emotion classification models in practical applications. The brain-based emotion recognition model proposed in this paper is able to sufficiently consider these individual differences. The proposed model comprises an emotion classification module and an individual difference module (IDM). The emotion classification module captures the spatial and temporal features of the EEG data, while the IDM introduces personalized adjustments to specific emotional features by accounting for participant-specific variations as a form of interference. This approach aims to enhance the classification performance of EEG-based emotion recognition for diverse participants. The results of our comparative experiments indicate that the proposed method obtains a maximum accuracy of 96.43% for binary classification on DEAP data. Furthermore, it performs better in scenarios with significant individual differences, where it reaches a maximum accuracy of 98.92%. MDPI 2023-09-08 /pmc/articles/PMC10535213/ /pubmed/37765808 http://dx.doi.org/10.3390/s23187749 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Xia, Yuxiao
Liu, Yinhua
EEG-Based Emotion Recognition with Consideration of Individual Difference
title EEG-Based Emotion Recognition with Consideration of Individual Difference
title_full EEG-Based Emotion Recognition with Consideration of Individual Difference
title_fullStr EEG-Based Emotion Recognition with Consideration of Individual Difference
title_full_unstemmed EEG-Based Emotion Recognition with Consideration of Individual Difference
title_short EEG-Based Emotion Recognition with Consideration of Individual Difference
title_sort eeg-based emotion recognition with consideration of individual difference
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10535213/
https://www.ncbi.nlm.nih.gov/pubmed/37765808
http://dx.doi.org/10.3390/s23187749
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