Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition
Due to some limitations of current heuristics and evolutionary algorithms, this paper proposed a new swarm based algorithm for feature selection method called Social Spider Optimization (SSO-FS). In this research, SSO-FS is used in the EEG-based emotion recognition model as searching method to find...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Science Publishing Corporation Inc
2018
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| Online Access: | https://repo.uum.edu.my/id/eprint/25274/1/IJET%207%202.15%202018%20148%20149.pdf |
| _version_ | 1825805256308555776 |
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| author | Al-Qammaz, Abdullah Yousef Ahmad, Farzana Kabir Yusof, Yuhanis |
| author_facet | Al-Qammaz, Abdullah Yousef Ahmad, Farzana Kabir Yusof, Yuhanis |
| author_sort | Al-Qammaz, Abdullah Yousef |
| collection | UUM |
| description | Due to some limitations of current heuristics and evolutionary algorithms, this paper proposed a new swarm based algorithm for feature selection method called Social Spider Optimization (SSO-FS). In this research, SSO-FS is used in the EEG-based emotion recognition model as searching method to find optimal feature set to maximize classification performance and mimics the cooperative behaviour and mechanism of social spiders in nature. This proposed feature selection method has been tested on DEAP EEG dataset with six subjects and compared with the most popular heuristic algorithms such as GA, PSO and ABC. The results show that the SSO-FS provides a remarkable
and comparable performance compared to other existing methods. Whereby, the max accuracy obtained is 66.66% and 70.83%, the mean
accuracy obtained is 55.51±7.17 and 60.97±8.38 for 3-level of valence emotions and 3-level of arousal emotions classification respectively. |
| first_indexed | 2024-07-04T06:29:19Z |
| format | Article |
| id | uum-25274 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T06:29:19Z |
| publishDate | 2018 |
| publisher | Science Publishing Corporation Inc |
| record_format | eprints |
| spelling | uum-252742018-12-11T01:13:20Z https://repo.uum.edu.my/id/eprint/25274/ Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition Al-Qammaz, Abdullah Yousef Ahmad, Farzana Kabir Yusof, Yuhanis QA75 Electronic computers. Computer science Due to some limitations of current heuristics and evolutionary algorithms, this paper proposed a new swarm based algorithm for feature selection method called Social Spider Optimization (SSO-FS). In this research, SSO-FS is used in the EEG-based emotion recognition model as searching method to find optimal feature set to maximize classification performance and mimics the cooperative behaviour and mechanism of social spiders in nature. This proposed feature selection method has been tested on DEAP EEG dataset with six subjects and compared with the most popular heuristic algorithms such as GA, PSO and ABC. The results show that the SSO-FS provides a remarkable and comparable performance compared to other existing methods. Whereby, the max accuracy obtained is 66.66% and 70.83%, the mean accuracy obtained is 55.51±7.17 and 60.97±8.38 for 3-level of valence emotions and 3-level of arousal emotions classification respectively. Science Publishing Corporation Inc 2018 Article PeerReviewed application/pdf en cc_by https://repo.uum.edu.my/id/eprint/25274/1/IJET%207%202.15%202018%20148%20149.pdf Al-Qammaz, Abdullah Yousef and Ahmad, Farzana Kabir and Yusof, Yuhanis (2018) Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition. International Journal of Engineering & Technology, 7 (2.15). pp. 146-149. ISSN 2227-524X http://doi.org/10.14419/ijet.v7i2.15.11373 doi:10.14419/ijet.v7i2.15.11373 doi:10.14419/ijet.v7i2.15.11373 |
| spellingShingle | QA75 Electronic computers. Computer science Al-Qammaz, Abdullah Yousef Ahmad, Farzana Kabir Yusof, Yuhanis Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition |
| title | Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition |
| title_full | Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition |
| title_fullStr | Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition |
| title_full_unstemmed | Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition |
| title_short | Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition |
| title_sort | social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition |
| topic | QA75 Electronic computers. Computer science |
| url | https://repo.uum.edu.my/id/eprint/25274/1/IJET%207%202.15%202018%20148%20149.pdf |
| work_keys_str_mv | AT alqammazabdullahyousef socialspideroptimisationalgorithmfordimensionreductionofelectroencephalogramsignalsinhumanemotionrecognition AT ahmadfarzanakabir socialspideroptimisationalgorithmfordimensionreductionofelectroencephalogramsignalsinhumanemotionrecognition AT yusofyuhanis socialspideroptimisationalgorithmfordimensionreductionofelectroencephalogramsignalsinhumanemotionrecognition |