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An Investigation of Deep Learning Models for EEG-Based Emotion Recognition

Emotion is the human brain reacting to objective things. In real life, human emotions are complex and changeable, so research into emotion recognition is of great significance in real life applications. Recently, many deep learning and machine learning methods have been widely applied in emotion rec...

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Autores principales: Zhang, Yaqing, Chen, Jinling, Tan, Jen Hong, Chen, Yuxuan, Chen, Yunyi, Li, Dihan, Yang, Lei, Su, Jian, Huang, Xin, Che, Wenliang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7785875/
https://www.ncbi.nlm.nih.gov/pubmed/33424547
http://dx.doi.org/10.3389/fnins.2020.622759
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author Zhang, Yaqing
Chen, Jinling
Tan, Jen Hong
Chen, Yuxuan
Chen, Yunyi
Li, Dihan
Yang, Lei
Su, Jian
Huang, Xin
Che, Wenliang
author_facet Zhang, Yaqing
Chen, Jinling
Tan, Jen Hong
Chen, Yuxuan
Chen, Yunyi
Li, Dihan
Yang, Lei
Su, Jian
Huang, Xin
Che, Wenliang
author_sort Zhang, Yaqing
collection PubMed
description Emotion is the human brain reacting to objective things. In real life, human emotions are complex and changeable, so research into emotion recognition is of great significance in real life applications. Recently, many deep learning and machine learning methods have been widely applied in emotion recognition based on EEG signals. However, the traditional machine learning method has a major disadvantage in that the feature extraction process is usually cumbersome, which relies heavily on human experts. Then, end-to-end deep learning methods emerged as an effective method to address this disadvantage with the help of raw signal features and time-frequency spectrums. Here, we investigated the application of several deep learning models to the research field of EEG-based emotion recognition, including deep neural networks (DNN), convolutional neural networks (CNN), long short-term memory (LSTM), and a hybrid model of CNN and LSTM (CNN-LSTM). The experiments were carried on the well-known DEAP dataset. Experimental results show that the CNN and CNN-LSTM models had high classification performance in EEG-based emotion recognition, and their accurate extraction rate of RAW data reached 90.12 and 94.17%, respectively. The performance of the DNN model was not as accurate as other models, but the training speed was fast. The LSTM model was not as stable as the CNN and CNN-LSTM models. Moreover, with the same number of parameters, the training speed of the LSTM was much slower and it was difficult to achieve convergence. Additional parameter comparison experiments with other models, including epoch, learning rate, and dropout probability, were also conducted in the paper. Comparison results prove that the DNN model converged to optimal with fewer epochs and a higher learning rate. In contrast, the CNN model needed more epochs to learn. As for dropout probability, reducing the parameters by ~50% each time was appropriate.
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spelling pubmed-77858752021-01-07 An Investigation of Deep Learning Models for EEG-Based Emotion Recognition Zhang, Yaqing Chen, Jinling Tan, Jen Hong Chen, Yuxuan Chen, Yunyi Li, Dihan Yang, Lei Su, Jian Huang, Xin Che, Wenliang Front Neurosci Neuroscience Emotion is the human brain reacting to objective things. In real life, human emotions are complex and changeable, so research into emotion recognition is of great significance in real life applications. Recently, many deep learning and machine learning methods have been widely applied in emotion recognition based on EEG signals. However, the traditional machine learning method has a major disadvantage in that the feature extraction process is usually cumbersome, which relies heavily on human experts. Then, end-to-end deep learning methods emerged as an effective method to address this disadvantage with the help of raw signal features and time-frequency spectrums. Here, we investigated the application of several deep learning models to the research field of EEG-based emotion recognition, including deep neural networks (DNN), convolutional neural networks (CNN), long short-term memory (LSTM), and a hybrid model of CNN and LSTM (CNN-LSTM). The experiments were carried on the well-known DEAP dataset. Experimental results show that the CNN and CNN-LSTM models had high classification performance in EEG-based emotion recognition, and their accurate extraction rate of RAW data reached 90.12 and 94.17%, respectively. The performance of the DNN model was not as accurate as other models, but the training speed was fast. The LSTM model was not as stable as the CNN and CNN-LSTM models. Moreover, with the same number of parameters, the training speed of the LSTM was much slower and it was difficult to achieve convergence. Additional parameter comparison experiments with other models, including epoch, learning rate, and dropout probability, were also conducted in the paper. Comparison results prove that the DNN model converged to optimal with fewer epochs and a higher learning rate. In contrast, the CNN model needed more epochs to learn. As for dropout probability, reducing the parameters by ~50% each time was appropriate. Frontiers Media S.A. 2020-12-23 /pmc/articles/PMC7785875/ /pubmed/33424547 http://dx.doi.org/10.3389/fnins.2020.622759 Text en Copyright © 2020 Zhang, Chen, Tan, Chen, Chen, Li, Yang, Su, Huang and Che. http://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
Zhang, Yaqing
Chen, Jinling
Tan, Jen Hong
Chen, Yuxuan
Chen, Yunyi
Li, Dihan
Yang, Lei
Su, Jian
Huang, Xin
Che, Wenliang
An Investigation of Deep Learning Models for EEG-Based Emotion Recognition
title An Investigation of Deep Learning Models for EEG-Based Emotion Recognition
title_full An Investigation of Deep Learning Models for EEG-Based Emotion Recognition
title_fullStr An Investigation of Deep Learning Models for EEG-Based Emotion Recognition
title_full_unstemmed An Investigation of Deep Learning Models for EEG-Based Emotion Recognition
title_short An Investigation of Deep Learning Models for EEG-Based Emotion Recognition
title_sort investigation of deep learning models for eeg-based emotion recognition
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7785875/
https://www.ncbi.nlm.nih.gov/pubmed/33424547
http://dx.doi.org/10.3389/fnins.2020.622759
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