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Deep Sparse Autoencoder and Recursive Neural Network for EEG Emotion Recognition
Recently, emotional electroencephalography (EEG) has been of great importance in brain–computer interfaces, and it is more urgent to realize automatic emotion recognition. The EEG signal has the disadvantages of being non-smooth, non-linear, stochastic, and susceptible to background noise. Additiona...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9497873/ https://www.ncbi.nlm.nih.gov/pubmed/36141073 http://dx.doi.org/10.3390/e24091187 |
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author | Li, Qi Liu, Yunqing Shang, Yujie Zhang, Qiong Yan, Fei |
author_facet | Li, Qi Liu, Yunqing Shang, Yujie Zhang, Qiong Yan, Fei |
author_sort | Li, Qi |
collection | PubMed |
description | Recently, emotional electroencephalography (EEG) has been of great importance in brain–computer interfaces, and it is more urgent to realize automatic emotion recognition. The EEG signal has the disadvantages of being non-smooth, non-linear, stochastic, and susceptible to background noise. Additionally, EEG signal processing network models have the disadvantages of a large number of parameters and long training time. To address the above issues, a novel model is presented in this paper. Initially, a deep sparse autoencoder network (DSAE) was used to remove redundant information from the EEG signal and reconstruct its underlying features. Further, combining a convolutional neural network (CNN) with long short-term memory (LSTM) can extract relevant features from task-related features, mine the correlation between the 32 channels of the EEG signal, and integrate contextual information from these frames. The proposed DSAE + CNN + LSTM (DCRNN) model was experimented with on the public dataset DEAP. The classification accuracies of valence and arousal reached 76.70% and 81.43%, respectively. Meanwhile, we conducted experiments with other comparative methods to further demonstrate the effectiveness of the DCRNN method. |
format | Online Article Text |
id | pubmed-9497873 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94978732022-09-23 Deep Sparse Autoencoder and Recursive Neural Network for EEG Emotion Recognition Li, Qi Liu, Yunqing Shang, Yujie Zhang, Qiong Yan, Fei Entropy (Basel) Article Recently, emotional electroencephalography (EEG) has been of great importance in brain–computer interfaces, and it is more urgent to realize automatic emotion recognition. The EEG signal has the disadvantages of being non-smooth, non-linear, stochastic, and susceptible to background noise. Additionally, EEG signal processing network models have the disadvantages of a large number of parameters and long training time. To address the above issues, a novel model is presented in this paper. Initially, a deep sparse autoencoder network (DSAE) was used to remove redundant information from the EEG signal and reconstruct its underlying features. Further, combining a convolutional neural network (CNN) with long short-term memory (LSTM) can extract relevant features from task-related features, mine the correlation between the 32 channels of the EEG signal, and integrate contextual information from these frames. The proposed DSAE + CNN + LSTM (DCRNN) model was experimented with on the public dataset DEAP. The classification accuracies of valence and arousal reached 76.70% and 81.43%, respectively. Meanwhile, we conducted experiments with other comparative methods to further demonstrate the effectiveness of the DCRNN method. MDPI 2022-08-25 /pmc/articles/PMC9497873/ /pubmed/36141073 http://dx.doi.org/10.3390/e24091187 Text en © 2022 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 Li, Qi Liu, Yunqing Shang, Yujie Zhang, Qiong Yan, Fei Deep Sparse Autoencoder and Recursive Neural Network for EEG Emotion Recognition |
title | Deep Sparse Autoencoder and Recursive Neural Network for EEG Emotion Recognition |
title_full | Deep Sparse Autoencoder and Recursive Neural Network for EEG Emotion Recognition |
title_fullStr | Deep Sparse Autoencoder and Recursive Neural Network for EEG Emotion Recognition |
title_full_unstemmed | Deep Sparse Autoencoder and Recursive Neural Network for EEG Emotion Recognition |
title_short | Deep Sparse Autoencoder and Recursive Neural Network for EEG Emotion Recognition |
title_sort | deep sparse autoencoder and recursive neural network for eeg emotion recognition |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9497873/ https://www.ncbi.nlm.nih.gov/pubmed/36141073 http://dx.doi.org/10.3390/e24091187 |
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