Accurate EEG-Based Emotion Recognition on Combined Features Using Deep Convolutional Neural Networks
In order to improve the accuracy of emotional recognition by end-to-end automatic learning of emotional features in spatial and temporal dimensions of electroencephalogram (EEG), an EEG emotional feature learning and classification method using deep convolution neural network (CNN) was proposed base...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8676231/ |
| _version_ | 1818323471955918848 |
|---|---|
| author | J. X. Chen P. W. Zhang Z. J. Mao Y. F. Huang D. M. Jiang Y. N. Zhang |
| author_facet | J. X. Chen P. W. Zhang Z. J. Mao Y. F. Huang D. M. Jiang Y. N. Zhang |
| author_sort | J. X. Chen |
| collection | DOAJ |
| description | In order to improve the accuracy of emotional recognition by end-to-end automatic learning of emotional features in spatial and temporal dimensions of electroencephalogram (EEG), an EEG emotional feature learning and classification method using deep convolution neural network (CNN) was proposed based on temporal features, frequential features, and their combinations of EEG signals in DEAP dataset. The shallow machine learning models including bagging tree (BT), support vector machine (SVM), linear discriminant analysis (LDA), and Bayesian linear discriminant analysis (BLDA) models and deep CNN models were used to make emotional binary classification experiments on DEAP datasets in valence and arousal dimensions. The experimental results showed that the deep CNN models which require no feature engineering achieved the best recognition performance on temporal and frequency combined features in both valence and arousal dimensions, which is 3.58% higher than the performance of the best traditional BT classifier in valence dimension and 3.29% higher than that of BT classifier in arousal dimension. |
| first_indexed | 2024-12-13T11:13:14Z |
| format | Article |
| id | doaj.art-6734f30b75054ef5be82814dbdee834e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T11:13:14Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6734f30b75054ef5be82814dbdee834e2022-12-21T23:48:41ZengIEEEIEEE Access2169-35362019-01-017443174432810.1109/ACCESS.2019.29082858676231Accurate EEG-Based Emotion Recognition on Combined Features Using Deep Convolutional Neural NetworksJ. X. Chen0https://orcid.org/0000-0002-1994-4752P. W. Zhang1Z. J. Mao2Y. F. Huang3D. M. Jiang4Y. N. Zhang5School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, ChinaDepartment of Electronical and Information Engineering, Shaanxi University of Science and Technology, Xi’an, ChinaDepartment of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX, USADepartment of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX, USASchool of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, ChinaSchool of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, ChinaIn order to improve the accuracy of emotional recognition by end-to-end automatic learning of emotional features in spatial and temporal dimensions of electroencephalogram (EEG), an EEG emotional feature learning and classification method using deep convolution neural network (CNN) was proposed based on temporal features, frequential features, and their combinations of EEG signals in DEAP dataset. The shallow machine learning models including bagging tree (BT), support vector machine (SVM), linear discriminant analysis (LDA), and Bayesian linear discriminant analysis (BLDA) models and deep CNN models were used to make emotional binary classification experiments on DEAP datasets in valence and arousal dimensions. The experimental results showed that the deep CNN models which require no feature engineering achieved the best recognition performance on temporal and frequency combined features in both valence and arousal dimensions, which is 3.58% higher than the performance of the best traditional BT classifier in valence dimension and 3.29% higher than that of BT classifier in arousal dimension.https://ieeexplore.ieee.org/document/8676231/EEGemotion recognitionconvolution neural networkcombined featuresdeep learning |
| spellingShingle | J. X. Chen P. W. Zhang Z. J. Mao Y. F. Huang D. M. Jiang Y. N. Zhang Accurate EEG-Based Emotion Recognition on Combined Features Using Deep Convolutional Neural Networks IEEE Access EEG emotion recognition convolution neural network combined features deep learning |
| title | Accurate EEG-Based Emotion Recognition on Combined Features Using Deep Convolutional Neural Networks |
| title_full | Accurate EEG-Based Emotion Recognition on Combined Features Using Deep Convolutional Neural Networks |
| title_fullStr | Accurate EEG-Based Emotion Recognition on Combined Features Using Deep Convolutional Neural Networks |
| title_full_unstemmed | Accurate EEG-Based Emotion Recognition on Combined Features Using Deep Convolutional Neural Networks |
| title_short | Accurate EEG-Based Emotion Recognition on Combined Features Using Deep Convolutional Neural Networks |
| title_sort | accurate eeg based emotion recognition on combined features using deep convolutional neural networks |
| topic | EEG emotion recognition convolution neural network combined features deep learning |
| url | https://ieeexplore.ieee.org/document/8676231/ |
| work_keys_str_mv | AT jxchen accurateeegbasedemotionrecognitiononcombinedfeaturesusingdeepconvolutionalneuralnetworks AT pwzhang accurateeegbasedemotionrecognitiononcombinedfeaturesusingdeepconvolutionalneuralnetworks AT zjmao accurateeegbasedemotionrecognitiononcombinedfeaturesusingdeepconvolutionalneuralnetworks AT yfhuang accurateeegbasedemotionrecognitiononcombinedfeaturesusingdeepconvolutionalneuralnetworks AT dmjiang accurateeegbasedemotionrecognitiononcombinedfeaturesusingdeepconvolutionalneuralnetworks AT ynzhang accurateeegbasedemotionrecognitiononcombinedfeaturesusingdeepconvolutionalneuralnetworks |