Enhancing BCI-Based Emotion Recognition Using an Improved Particle Swarm Optimization for Feature Selection
Electroencephalogram (EEG) signals have been widely used in emotion recognition. However, the current EEG-based emotion recognition has low accuracy of emotion classification, and its real-time application is limited. In order to address these issues, in this paper, we proposed an improved feature s...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/11/3028 |
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| author | Zina Li Lina Qiu Ruixin Li Zhipeng He Jun Xiao Yan Liang Fei Wang Jiahui Pan |
| author_facet | Zina Li Lina Qiu Ruixin Li Zhipeng He Jun Xiao Yan Liang Fei Wang Jiahui Pan |
| author_sort | Zina Li |
| collection | DOAJ |
| description | Electroencephalogram (EEG) signals have been widely used in emotion recognition. However, the current EEG-based emotion recognition has low accuracy of emotion classification, and its real-time application is limited. In order to address these issues, in this paper, we proposed an improved feature selection algorithm to recognize subjects’ emotion states based on EEG signal, and combined this feature selection method to design an online emotion recognition brain-computer interface (BCI) system. Specifically, first, different dimensional features from the time-domain, frequency domain, and time-frequency domain were extracted. Then, a modified particle swarm optimization (PSO) method with multi-stage linearly-decreasing inertia weight (MLDW) was purposed for feature selection. The MLDW algorithm can be used to easily refine the process of decreasing the inertia weight. Finally, the emotion types were classified by the support vector machine classifier. We extracted different features from the EEG data in the DEAP data set collected by 32 subjects to perform two offline experiments. Our results showed that the average accuracy of four-class emotion recognition reached 76.67%. Compared with the latest benchmark, our proposed MLDW-PSO feature selection improves the accuracy of EEG-based emotion recognition. To further validate the efficiency of the MLDW-PSO feature selection method, we developed an online two-class emotion recognition system evoked by Chinese videos, which achieved good performance for 10 healthy subjects with an average accuracy of 89.5%. The effectiveness of our method was thus demonstrated. |
| first_indexed | 2024-03-10T19:34:11Z |
| format | Article |
| id | doaj.art-e66e8975703d4f7f8c6e17b6c369eea2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T19:34:11Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e66e8975703d4f7f8c6e17b6c369eea22023-11-20T01:52:01ZengMDPI AGSensors1424-82202020-05-012011302810.3390/s20113028Enhancing BCI-Based Emotion Recognition Using an Improved Particle Swarm Optimization for Feature SelectionZina Li0Lina Qiu1Ruixin Li2Zhipeng He3Jun Xiao4Yan Liang5Fei Wang6Jiahui Pan7School of Software, South China Normal University, Guangzhou 510631, ChinaSchool of Software, South China Normal University, Guangzhou 510631, ChinaSchool of Software, South China Normal University, Guangzhou 510631, ChinaSchool of Software, South China Normal University, Guangzhou 510631, ChinaSchool of Electric Power Engineering, South China University of Technology, Guangzhou 510640, ChinaSchool of Software, South China Normal University, Guangzhou 510631, ChinaSchool of Software, South China Normal University, Guangzhou 510631, ChinaSchool of Software, South China Normal University, Guangzhou 510631, ChinaElectroencephalogram (EEG) signals have been widely used in emotion recognition. However, the current EEG-based emotion recognition has low accuracy of emotion classification, and its real-time application is limited. In order to address these issues, in this paper, we proposed an improved feature selection algorithm to recognize subjects’ emotion states based on EEG signal, and combined this feature selection method to design an online emotion recognition brain-computer interface (BCI) system. Specifically, first, different dimensional features from the time-domain, frequency domain, and time-frequency domain were extracted. Then, a modified particle swarm optimization (PSO) method with multi-stage linearly-decreasing inertia weight (MLDW) was purposed for feature selection. The MLDW algorithm can be used to easily refine the process of decreasing the inertia weight. Finally, the emotion types were classified by the support vector machine classifier. We extracted different features from the EEG data in the DEAP data set collected by 32 subjects to perform two offline experiments. Our results showed that the average accuracy of four-class emotion recognition reached 76.67%. Compared with the latest benchmark, our proposed MLDW-PSO feature selection improves the accuracy of EEG-based emotion recognition. To further validate the efficiency of the MLDW-PSO feature selection method, we developed an online two-class emotion recognition system evoked by Chinese videos, which achieved good performance for 10 healthy subjects with an average accuracy of 89.5%. The effectiveness of our method was thus demonstrated.https://www.mdpi.com/1424-8220/20/11/3028electroencephalography (EEG)brain-computer interface (BCI)emotion recognitionfeature selectionparticle swarm optimization (PSO) |
| spellingShingle | Zina Li Lina Qiu Ruixin Li Zhipeng He Jun Xiao Yan Liang Fei Wang Jiahui Pan Enhancing BCI-Based Emotion Recognition Using an Improved Particle Swarm Optimization for Feature Selection Sensors electroencephalography (EEG) brain-computer interface (BCI) emotion recognition feature selection particle swarm optimization (PSO) |
| title | Enhancing BCI-Based Emotion Recognition Using an Improved Particle Swarm Optimization for Feature Selection |
| title_full | Enhancing BCI-Based Emotion Recognition Using an Improved Particle Swarm Optimization for Feature Selection |
| title_fullStr | Enhancing BCI-Based Emotion Recognition Using an Improved Particle Swarm Optimization for Feature Selection |
| title_full_unstemmed | Enhancing BCI-Based Emotion Recognition Using an Improved Particle Swarm Optimization for Feature Selection |
| title_short | Enhancing BCI-Based Emotion Recognition Using an Improved Particle Swarm Optimization for Feature Selection |
| title_sort | enhancing bci based emotion recognition using an improved particle swarm optimization for feature selection |
| topic | electroencephalography (EEG) brain-computer interface (BCI) emotion recognition feature selection particle swarm optimization (PSO) |
| url | https://www.mdpi.com/1424-8220/20/11/3028 |
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