Construction and Analysis of Emotion Computing Model Based on LSTM
The electroencephalogram (EEG) is the most common method used to study emotions and capture electrical brain activity changes. Long short-term memory (LSTM) processes the temporal characteristics of data and is mostly used for emotional text and speech recognition. Since an EEG involves a time serie...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/8897105 |
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| _version_ | 1832554126450360320 |
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| author | Huiping Jiang Rui Jiao Zequn Wang Ting Zhang Licheng Wu |
| author_facet | Huiping Jiang Rui Jiao Zequn Wang Ting Zhang Licheng Wu |
| author_sort | Huiping Jiang |
| collection | DOAJ |
| description | The electroencephalogram (EEG) is the most common method used to study emotions and capture electrical brain activity changes. Long short-term memory (LSTM) processes the temporal characteristics of data and is mostly used for emotional text and speech recognition. Since an EEG involves a time series signal, this article mainly studied the introduction of LSTM for emotional EEG recognition. First, an ALL-LSTM model with a four-layered LSTM network was established in which the average accuracy rate for emotional classification reached 86.48%. Second, four EEG characteristics were extracted via the wavelet transform (WT) using the LSTM-based sentiment classification network. The experimental results showed that the best average classification accuracy of these four features was 73.48%. This was 13% lower than in the ALL-LSTM model, indicating that inappropriate feature extraction methods could destroy the timing of EEG signals. LSTM can be used to thoroughly examine EEG signal timing and preprocessed EEG data. The accuracy and stability of the ALL-LSTM model are significantly superior to those of the WT-LSTM model. The result showed that the process of emotion generation based on EEG is sequential. Compared with EEG emotion extraction using WT, the raw EEG signal’s timing is more suitable for the LSTM network. |
| format | Article |
| id | doaj-art-fc7f2071cb0d4e8ebbed94f1f7724218 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-fc7f2071cb0d4e8ebbed94f1f77242182025-02-03T05:52:26ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/88971058897105Construction and Analysis of Emotion Computing Model Based on LSTMHuiping Jiang0Rui Jiao1Zequn Wang2Ting Zhang3Licheng Wu4Brain Cognitive Computing Lab, School of Information and Engineering, Minzu University of China, Beijing 100081, ChinaBrain Cognitive Computing Lab, School of Information and Engineering, Minzu University of China, Beijing 100081, ChinaBrain Cognitive Computing Lab, School of Information and Engineering, Minzu University of China, Beijing 100081, ChinaBrain Cognitive Computing Lab, School of Information and Engineering, Minzu University of China, Beijing 100081, ChinaBrain Cognitive Computing Lab, School of Information and Engineering, Minzu University of China, Beijing 100081, ChinaThe electroencephalogram (EEG) is the most common method used to study emotions and capture electrical brain activity changes. Long short-term memory (LSTM) processes the temporal characteristics of data and is mostly used for emotional text and speech recognition. Since an EEG involves a time series signal, this article mainly studied the introduction of LSTM for emotional EEG recognition. First, an ALL-LSTM model with a four-layered LSTM network was established in which the average accuracy rate for emotional classification reached 86.48%. Second, four EEG characteristics were extracted via the wavelet transform (WT) using the LSTM-based sentiment classification network. The experimental results showed that the best average classification accuracy of these four features was 73.48%. This was 13% lower than in the ALL-LSTM model, indicating that inappropriate feature extraction methods could destroy the timing of EEG signals. LSTM can be used to thoroughly examine EEG signal timing and preprocessed EEG data. The accuracy and stability of the ALL-LSTM model are significantly superior to those of the WT-LSTM model. The result showed that the process of emotion generation based on EEG is sequential. Compared with EEG emotion extraction using WT, the raw EEG signal’s timing is more suitable for the LSTM network.http://dx.doi.org/10.1155/2021/8897105 |
| spellingShingle | Huiping Jiang Rui Jiao Zequn Wang Ting Zhang Licheng Wu Construction and Analysis of Emotion Computing Model Based on LSTM Complexity |
| title | Construction and Analysis of Emotion Computing Model Based on LSTM |
| title_full | Construction and Analysis of Emotion Computing Model Based on LSTM |
| title_fullStr | Construction and Analysis of Emotion Computing Model Based on LSTM |
| title_full_unstemmed | Construction and Analysis of Emotion Computing Model Based on LSTM |
| title_short | Construction and Analysis of Emotion Computing Model Based on LSTM |
| title_sort | construction and analysis of emotion computing model based on lstm |
| url | http://dx.doi.org/10.1155/2021/8897105 |
| work_keys_str_mv | AT huipingjiang constructionandanalysisofemotioncomputingmodelbasedonlstm AT ruijiao constructionandanalysisofemotioncomputingmodelbasedonlstm AT zequnwang constructionandanalysisofemotioncomputingmodelbasedonlstm AT tingzhang constructionandanalysisofemotioncomputingmodelbasedonlstm AT lichengwu constructionandanalysisofemotioncomputingmodelbasedonlstm |