Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals
Feature selection plays a crucial role in analyzing huge-volume, high-dimensional EEG signals in human-centered automation systems. However, classical feature selection methods pay little attention to transferring cross-subject information for emotions. To perform cross-subject emotion recognition,...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-05-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/11/5/683 |
| _version_ | 1818033552712794112 |
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| author | Jiahui Cai Wei Chen Zhong Yin |
| author_facet | Jiahui Cai Wei Chen Zhong Yin |
| author_sort | Jiahui Cai |
| collection | DOAJ |
| description | Feature selection plays a crucial role in analyzing huge-volume, high-dimensional EEG signals in human-centered automation systems. However, classical feature selection methods pay little attention to transferring cross-subject information for emotions. To perform cross-subject emotion recognition, a classifier able to utilize EEG data to train a general model suitable for different subjects is needed. However, existing methods are imprecise due to the fact that the effective feelings of individuals are personalized. In this work, the cross-subject emotion recognition model on both binary and multi affective states are developed based on the newly designed multiple transferable recursive feature elimination (M-TRFE). M-TRFE manages not only a stricter feature selection of all subjects to discover the most robust features but also a unique subject selection to decide the most trusted subjects for certain emotions. Via a least square support vector machine (LSSVM), the overall multi (joy, peace, anger and depression) classification accuracy of the proposed M-TRFE reaches 0.6513, outperforming all other methods used or referenced in this paper. |
| first_indexed | 2024-12-10T06:25:05Z |
| format | Article |
| id | doaj.art-e4f9cea4757a490990c75db21336f873 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-12-10T06:25:05Z |
| publishDate | 2019-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-e4f9cea4757a490990c75db21336f8732022-12-22T01:59:15ZengMDPI AGSymmetry2073-89942019-05-0111568310.3390/sym11050683sym11050683Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG SignalsJiahui Cai0Wei Chen1Zhong Yin2School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaFeature selection plays a crucial role in analyzing huge-volume, high-dimensional EEG signals in human-centered automation systems. However, classical feature selection methods pay little attention to transferring cross-subject information for emotions. To perform cross-subject emotion recognition, a classifier able to utilize EEG data to train a general model suitable for different subjects is needed. However, existing methods are imprecise due to the fact that the effective feelings of individuals are personalized. In this work, the cross-subject emotion recognition model on both binary and multi affective states are developed based on the newly designed multiple transferable recursive feature elimination (M-TRFE). M-TRFE manages not only a stricter feature selection of all subjects to discover the most robust features but also a unique subject selection to decide the most trusted subjects for certain emotions. Via a least square support vector machine (LSSVM), the overall multi (joy, peace, anger and depression) classification accuracy of the proposed M-TRFE reaches 0.6513, outperforming all other methods used or referenced in this paper.https://www.mdpi.com/2073-8994/11/5/683emotion recognitioneffective computingphysiological signalsrecursive feature eliminationEEG |
| spellingShingle | Jiahui Cai Wei Chen Zhong Yin Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals Symmetry emotion recognition effective computing physiological signals recursive feature elimination EEG |
| title | Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals |
| title_full | Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals |
| title_fullStr | Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals |
| title_full_unstemmed | Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals |
| title_short | Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals |
| title_sort | multiple transferable recursive feature elimination technique for emotion recognition based on eeg signals |
| topic | emotion recognition effective computing physiological signals recursive feature elimination EEG |
| url | https://www.mdpi.com/2073-8994/11/5/683 |
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