Embedded EEG Feature Selection for Multi-Dimension Emotion Recognition via Local and Global Label Relevance
Due to the problem of a small amount of EEG samples and relatively high dimensionality of electroencephalogram (EEG) features, feature selection plays an essential role in EEG-based emotion recognition. However, current EEG-based emotion recognition studies utilize a problem transformation approach...
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| Published in | IEEE transactions on neural systems and rehabilitation engineering Vol. 32; pp. 514 - 526 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
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United States
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
2024
IEEE |
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| Abstract | Due to the problem of a small amount of EEG samples and relatively high dimensionality of electroencephalogram (EEG) features, feature selection plays an essential role in EEG-based emotion recognition. However, current EEG-based emotion recognition studies utilize a problem transformation approach to transform multi-dimension emotional labels into single-dimension labels, and then implement commonly used single-label feature selection methods to search feature subsets, which ignores the relations between different emotional dimensions. To tackle the problem, we propose an efficient EEG feature selection method for multi-dimension emotion recognition (EFSMDER) via local and global label relevance. First, to capture the local label correlations, EFSMDER implements orthogonal regression to map the original EEG feature space into a low-dimension space. Then, it employs the global label correlations in the original multi-dimension emotional label space to effectively construct the label information in the low-dimension space. With the aid of local and global relevance information, EFSMDER can conduct representational EEG feature subset selection. Three EEG emotional databases with multi-dimension emotional labels were used for performance comparison between EFSMDER and fourteen state-of-the-art methods, and the EFSMDER method achieves the best multi-dimension classification accuracies of 86.43, 84.80, and 97.86 percent on the DREAMER, DEAP, and HDED datasets, respectively. |
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| AbstractList | Due to the problem of a small amount of EEG samples and relatively high dimensionality of electroencephalogram (EEG) features, feature selection plays an essential role in EEG-based emotion recognition. However, current EEG-based emotion recognition studies utilize a problem transformation approach to transform multi-dimension emotional labels into single-dimension labels, and then implement commonly used single-label feature selection methods to search feature subsets, which ignores the relations between different emotional dimensions. To tackle the problem, we propose an efficient EEG feature selection method for multi-dimension emotion recognition (EFSMDER) via local and global label relevance. First, to capture the local label correlations, EFSMDER implements orthogonal regression to map the original EEG feature space into a low-dimension space. Then, it employs the global label correlations in the original multi-dimension emotional label space to effectively construct the label information in the low-dimension space. With the aid of local and global relevance information, EFSMDER can conduct representational EEG feature subset selection. Three EEG emotional databases with multi-dimension emotional labels were used for performance comparison between EFSMDER and fourteen state-of-the-art methods, and the EFSMDER method achieves the best multi-dimension classification accuracies of 86.43, 84.80, and 97.86 percent on the DREAMER, DEAP, and HDED datasets, respectively.Due to the problem of a small amount of EEG samples and relatively high dimensionality of electroencephalogram (EEG) features, feature selection plays an essential role in EEG-based emotion recognition. However, current EEG-based emotion recognition studies utilize a problem transformation approach to transform multi-dimension emotional labels into single-dimension labels, and then implement commonly used single-label feature selection methods to search feature subsets, which ignores the relations between different emotional dimensions. To tackle the problem, we propose an efficient EEG feature selection method for multi-dimension emotion recognition (EFSMDER) via local and global label relevance. First, to capture the local label correlations, EFSMDER implements orthogonal regression to map the original EEG feature space into a low-dimension space. Then, it employs the global label correlations in the original multi-dimension emotional label space to effectively construct the label information in the low-dimension space. With the aid of local and global relevance information, EFSMDER can conduct representational EEG feature subset selection. Three EEG emotional databases with multi-dimension emotional labels were used for performance comparison between EFSMDER and fourteen state-of-the-art methods, and the EFSMDER method achieves the best multi-dimension classification accuracies of 86.43, 84.80, and 97.86 percent on the DREAMER, DEAP, and HDED datasets, respectively. Due to the problem of a small amount of EEG samples and relatively high dimensionality of electroencephalogram (EEG) features, feature selection plays an essential role in EEG-based emotion recognition. However, current EEG-based emotion recognition studies utilize a problem transformation approach to transform multi-dimension emotional labels into single-dimension labels, and then implement commonly used single-label feature selection methods to search feature subsets, which ignores the relations between different emotional dimensions. To tackle the problem, we propose an efficient EEG feature selection method for multi-dimension emotion recognition (EFSMDER) via local and global label relevance. First, to capture the local label correlations, EFSMDER implements orthogonal regression to map the original EEG feature space into a low-dimension space. Then, it employs the global label correlations in the original multi-dimension emotional label space to effectively construct the label information in the low-dimension space. With the aid of local and global relevance information, EFSMDER can conduct representational EEG feature subset selection. Three EEG emotional databases with multi-dimension emotional labels were used for performance comparison between EFSMDER and fourteen state-of-the-art methods, and the EFSMDER method achieves the best multi-dimension classification accuracies of 86.43, 84.80, and 97.86 percent on the DREAMER, DEAP, and HDED datasets, respectively. |
| Author | Zhuo, Li Zhang, Hui Li, Xiaoguang Xu, Xueyuan Wei, Fulin Jia, Tianyuan Wu, Xia |
| Author_xml | – sequence: 1 givenname: Xueyuan surname: Xu fullname: Xu, Xueyuan organization: Faculty of Information Technology and the Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China – sequence: 2 givenname: Fulin orcidid: 0000-0003-1962-0675 surname: Wei fullname: Wei, Fulin organization: School of Artificial Intelligence, Beijing Normal University, Beijing, China – sequence: 3 givenname: Tianyuan surname: Jia fullname: Jia, Tianyuan organization: School of Artificial Intelligence, Beijing Normal University, Beijing, China – sequence: 4 givenname: Li orcidid: 0000-0002-9937-2669 surname: Zhuo fullname: Zhuo, Li organization: Faculty of Information Technology and the Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China – sequence: 5 givenname: Hui orcidid: 0000-0001-8012-4684 surname: Zhang fullname: Zhang, Hui organization: Faculty of Information Technology and the Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China – sequence: 6 givenname: Xiaoguang orcidid: 0000-0002-7307-6263 surname: Li fullname: Li, Xiaoguang organization: Faculty of Information Technology and the Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China – sequence: 7 givenname: Xia orcidid: 0000-0002-2377-6093 surname: Wu fullname: Wu, Xia organization: School of Artificial Intelligence, Beijing Normal University, Beijing, China |
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| SubjectTerms | Databases, Factual EEG Electroencephalogram Electroencephalography Electroencephalography - methods Emotion recognition Emotions Feature selection global relevance Humans Labels multi-dimension emotional labels Multidimensional methods Recognition, Psychology |
| Title | Embedded EEG Feature Selection for Multi-Dimension Emotion Recognition via Local and Global Label Relevance |
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