EEG-Based Emotion Recognition Using a 2D CNN with Different Kernels
Emotion recognition is receiving significant attention in research on health care and Human-Computer Interaction (HCI). Due to the high correlation with emotion and the capability to affect deceptive external expressions such as voices and faces, Electroencephalogram (EEG) based emotion recognition...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/9/6/231 |
| _version_ | 1797489964493045760 |
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| author | Yuqi Wang Lijun Zhang Pan Xia Peng Wang Xianxiang Chen Lidong Du Zhen Fang Mingyan Du |
| author_facet | Yuqi Wang Lijun Zhang Pan Xia Peng Wang Xianxiang Chen Lidong Du Zhen Fang Mingyan Du |
| author_sort | Yuqi Wang |
| collection | DOAJ |
| description | Emotion recognition is receiving significant attention in research on health care and Human-Computer Interaction (HCI). Due to the high correlation with emotion and the capability to affect deceptive external expressions such as voices and faces, Electroencephalogram (EEG) based emotion recognition methods have been globally accepted and widely applied. Recently, great improvements have been made in the development of machine learning for EEG-based emotion detection. However, there are still some major disadvantages in previous studies. Firstly, traditional machine learning methods require extracting features manually which is time-consuming and rely heavily on human experts. Secondly, to improve the model accuracies, many researchers used user-dependent models that lack generalization and universality. Moreover, there is still room for improvement in the recognition accuracies in most studies. Therefore, to overcome these shortcomings, an EEG-based novel deep neural network is proposed for emotion classification in this article. The proposed 2D CNN uses two convolutional kernels of different sizes to extract emotion-related features along both the time direction and the spatial direction. To verify the feasibility of the proposed model, the pubic emotion dataset DEAP is used in experiments. The results show accuracies of up to 99.99% and 99.98 for arousal and valence binary classification, respectively, which are encouraging for research and applications in the emotion recognition field. |
| first_indexed | 2024-03-10T00:24:12Z |
| format | Article |
| id | doaj.art-dd0242702ca04676aa385c1b56fb4615 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-10T00:24:12Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-dd0242702ca04676aa385c1b56fb46152023-11-23T15:37:44ZengMDPI AGBioengineering2306-53542022-05-019623110.3390/bioengineering9060231EEG-Based Emotion Recognition Using a 2D CNN with Different KernelsYuqi Wang0Lijun Zhang1Pan Xia2Peng Wang3Xianxiang Chen4Lidong Du5Zhen Fang6Mingyan Du7Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, ChinaInstitute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaChina Beijing Luhe Hospital, Capital Medical University, Beijing 101199, ChinaEmotion recognition is receiving significant attention in research on health care and Human-Computer Interaction (HCI). Due to the high correlation with emotion and the capability to affect deceptive external expressions such as voices and faces, Electroencephalogram (EEG) based emotion recognition methods have been globally accepted and widely applied. Recently, great improvements have been made in the development of machine learning for EEG-based emotion detection. However, there are still some major disadvantages in previous studies. Firstly, traditional machine learning methods require extracting features manually which is time-consuming and rely heavily on human experts. Secondly, to improve the model accuracies, many researchers used user-dependent models that lack generalization and universality. Moreover, there is still room for improvement in the recognition accuracies in most studies. Therefore, to overcome these shortcomings, an EEG-based novel deep neural network is proposed for emotion classification in this article. The proposed 2D CNN uses two convolutional kernels of different sizes to extract emotion-related features along both the time direction and the spatial direction. To verify the feasibility of the proposed model, the pubic emotion dataset DEAP is used in experiments. The results show accuracies of up to 99.99% and 99.98 for arousal and valence binary classification, respectively, which are encouraging for research and applications in the emotion recognition field.https://www.mdpi.com/2306-5354/9/6/231emotion recognitionmachine learningconvolutional neural networkelectroencephalogram |
| spellingShingle | Yuqi Wang Lijun Zhang Pan Xia Peng Wang Xianxiang Chen Lidong Du Zhen Fang Mingyan Du EEG-Based Emotion Recognition Using a 2D CNN with Different Kernels Bioengineering emotion recognition machine learning convolutional neural network electroencephalogram |
| title | EEG-Based Emotion Recognition Using a 2D CNN with Different Kernels |
| title_full | EEG-Based Emotion Recognition Using a 2D CNN with Different Kernels |
| title_fullStr | EEG-Based Emotion Recognition Using a 2D CNN with Different Kernels |
| title_full_unstemmed | EEG-Based Emotion Recognition Using a 2D CNN with Different Kernels |
| title_short | EEG-Based Emotion Recognition Using a 2D CNN with Different Kernels |
| title_sort | eeg based emotion recognition using a 2d cnn with different kernels |
| topic | emotion recognition machine learning convolutional neural network electroencephalogram |
| url | https://www.mdpi.com/2306-5354/9/6/231 |
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