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A EEG-based emotion recognition model with rhythm and time characteristics

As an advanced function of the human brain, emotion has a significant influence on human studies, works, and other aspects of life. Artificial Intelligence has played an important role in recognizing human emotion correctly. EEG-based emotion recognition (ER), one application of Brain Computer Inter...

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Detalles Bibliográficos
Autores principales: Yan, Jianzhuo, Chen, Shangbin, Deng, Sinuo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6757079/
https://www.ncbi.nlm.nih.gov/pubmed/31549331
http://dx.doi.org/10.1186/s40708-019-0100-y
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author Yan, Jianzhuo
Chen, Shangbin
Deng, Sinuo
author_facet Yan, Jianzhuo
Chen, Shangbin
Deng, Sinuo
author_sort Yan, Jianzhuo
collection PubMed
description As an advanced function of the human brain, emotion has a significant influence on human studies, works, and other aspects of life. Artificial Intelligence has played an important role in recognizing human emotion correctly. EEG-based emotion recognition (ER), one application of Brain Computer Interface (BCI), is becoming more popular in recent years. However, due to the ambiguity of human emotions and the complexity of EEG signals, the EEG-ER system which can recognize emotions with high accuracy is not easy to achieve. Based on the time scale, this paper chooses the recurrent neural network as the breakthrough point of the screening model. According to the rhythmic characteristics and temporal memory characteristics of EEG, this research proposes a Rhythmic Time EEG Emotion Recognition Model (RT-ERM) based on the valence and arousal of Long–Short-Term Memory Network (LSTM). By applying this model, the classification results of different rhythms and time scales are different. The optimal rhythm and time scale of the RT-ERM model are obtained through the results of the classification accuracy of different rhythms and different time scales. Then, the classification of emotional EEG is carried out by the best time scales corresponding to different rhythms. Finally, by comparing with other existing emotional EEG classification methods, it is found that the rhythm and time scale of the model can contribute to the accuracy of RT-ERM.
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spelling pubmed-67570792019-10-07 A EEG-based emotion recognition model with rhythm and time characteristics Yan, Jianzhuo Chen, Shangbin Deng, Sinuo Brain Inform Research As an advanced function of the human brain, emotion has a significant influence on human studies, works, and other aspects of life. Artificial Intelligence has played an important role in recognizing human emotion correctly. EEG-based emotion recognition (ER), one application of Brain Computer Interface (BCI), is becoming more popular in recent years. However, due to the ambiguity of human emotions and the complexity of EEG signals, the EEG-ER system which can recognize emotions with high accuracy is not easy to achieve. Based on the time scale, this paper chooses the recurrent neural network as the breakthrough point of the screening model. According to the rhythmic characteristics and temporal memory characteristics of EEG, this research proposes a Rhythmic Time EEG Emotion Recognition Model (RT-ERM) based on the valence and arousal of Long–Short-Term Memory Network (LSTM). By applying this model, the classification results of different rhythms and time scales are different. The optimal rhythm and time scale of the RT-ERM model are obtained through the results of the classification accuracy of different rhythms and different time scales. Then, the classification of emotional EEG is carried out by the best time scales corresponding to different rhythms. Finally, by comparing with other existing emotional EEG classification methods, it is found that the rhythm and time scale of the model can contribute to the accuracy of RT-ERM. Springer Berlin Heidelberg 2019-09-23 /pmc/articles/PMC6757079/ /pubmed/31549331 http://dx.doi.org/10.1186/s40708-019-0100-y Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research
Yan, Jianzhuo
Chen, Shangbin
Deng, Sinuo
A EEG-based emotion recognition model with rhythm and time characteristics
title A EEG-based emotion recognition model with rhythm and time characteristics
title_full A EEG-based emotion recognition model with rhythm and time characteristics
title_fullStr A EEG-based emotion recognition model with rhythm and time characteristics
title_full_unstemmed A EEG-based emotion recognition model with rhythm and time characteristics
title_short A EEG-based emotion recognition model with rhythm and time characteristics
title_sort eeg-based emotion recognition model with rhythm and time characteristics
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6757079/
https://www.ncbi.nlm.nih.gov/pubmed/31549331
http://dx.doi.org/10.1186/s40708-019-0100-y
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