Feature Optimization of EEG Signals Based on Ant Colony Algorithm
EEG signal can be understood as a kind of bioelectrical signal, which can reflect emotional information when the body is in different emotional states. However, the data collected are often high-dimensional. including many irrelevant or redundant features. The high-dimensional features make the spac...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2023-01-01
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| Series: | BIO Web of Conferences |
| Online Access: | https://www.bio-conferences.org/articles/bioconf/pdf/2023/04/bioconf_icbb2023_03012.pdf |
| _version_ | 1797829392508911616 |
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| author | Shengjie Zhang Rongkai Pan Guanglu Liu |
| author_facet | Shengjie Zhang Rongkai Pan Guanglu Liu |
| author_sort | Shengjie Zhang |
| collection | DOAJ |
| description | EEG signal can be understood as a kind of bioelectrical signal, which can reflect emotional information when the body is in different emotional states. However, the data collected are often high-dimensional. including many irrelevant or redundant features. The high-dimensional features make the space cost increase exponentially, which brings many difficulties to the research. Ant colony optimization algorithm, a swarm intelligence algorithm, can be used for feature selection. Ant colony optimization algorithm is used for feature selection of EEG signals. The feature subset to be selected is trained cooperatively and learned actively. The classification accuracy is evaluated through convolutional neural network, and the optimal subset is selected from the iterative local optimal solution. The results show that the ant colony optimization algorithm can effectively reduce the time complexity and calculation cost, Improve the accuracy of classification. |
| first_indexed | 2024-04-09T13:19:42Z |
| format | Article |
| id | doaj.art-36803edf5e8d44ab94d59a90c80bd62f |
| institution | Directory Open Access Journal |
| issn | 2117-4458 |
| language | English |
| last_indexed | 2024-04-09T13:19:42Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | BIO Web of Conferences |
| spelling | doaj.art-36803edf5e8d44ab94d59a90c80bd62f2023-05-11T09:08:11ZengEDP SciencesBIO Web of Conferences2117-44582023-01-01590301210.1051/bioconf/20235903012bioconf_icbb2023_03012Feature Optimization of EEG Signals Based on Ant Colony AlgorithmShengjie Zhang0Rongkai Pan1Guanglu Liu2School of electronic Information, Xi’an Polytechnic UniversitySchool of electronic Information, Xi’an Polytechnic UniversitySchool of electronic Information, Xi’an Polytechnic UniversityEEG signal can be understood as a kind of bioelectrical signal, which can reflect emotional information when the body is in different emotional states. However, the data collected are often high-dimensional. including many irrelevant or redundant features. The high-dimensional features make the space cost increase exponentially, which brings many difficulties to the research. Ant colony optimization algorithm, a swarm intelligence algorithm, can be used for feature selection. Ant colony optimization algorithm is used for feature selection of EEG signals. The feature subset to be selected is trained cooperatively and learned actively. The classification accuracy is evaluated through convolutional neural network, and the optimal subset is selected from the iterative local optimal solution. The results show that the ant colony optimization algorithm can effectively reduce the time complexity and calculation cost, Improve the accuracy of classification.https://www.bio-conferences.org/articles/bioconf/pdf/2023/04/bioconf_icbb2023_03012.pdf |
| spellingShingle | Shengjie Zhang Rongkai Pan Guanglu Liu Feature Optimization of EEG Signals Based on Ant Colony Algorithm BIO Web of Conferences |
| title | Feature Optimization of EEG Signals Based on Ant Colony Algorithm |
| title_full | Feature Optimization of EEG Signals Based on Ant Colony Algorithm |
| title_fullStr | Feature Optimization of EEG Signals Based on Ant Colony Algorithm |
| title_full_unstemmed | Feature Optimization of EEG Signals Based on Ant Colony Algorithm |
| title_short | Feature Optimization of EEG Signals Based on Ant Colony Algorithm |
| title_sort | feature optimization of eeg signals based on ant colony algorithm |
| url | https://www.bio-conferences.org/articles/bioconf/pdf/2023/04/bioconf_icbb2023_03012.pdf |
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