EEG Channel Selection Using Multiobjective Cuckoo Search for Person Identification as Protection System in Healthcare Applications
Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. Several studies defined the EEG with unique features, universality, and natural robustness to be used as a new track to prevent spoofing attacks. The EEG signals are a visual re...
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| creator | Abdi Alkareem Alyasseri, Zaid Alomari, Osama Ahmad Al-Betar, Mohammed Azmi Awadallah, Mohammed A. Hameed Abdulkareem, Karrar Abed Mohammed, Mazin Kadry, Seifedine Rajinikanth, V. Rho, Seungmin |
| description | Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. Several studies defined the EEG with unique features, universality, and natural robustness to be used as a new track to prevent spoofing attacks. The EEG signals are a visual recording of the brain’s electrical activities, measured by placing electrodes (channels) in various scalp positions. However, traditional EEG-based systems lead to high complexity with many channels, and some channels have critical information for the identification system while others do not. Several studies have proposed a single objective to address the EEG channel for person identification. Unfortunately, these studies only focused on increasing the accuracy rate without balancing the accuracy and the total number of selected EEG channels. The novelty of this paper is to propose a multiobjective binary version of the cuckoo search algorithm (MOBCS-KNN) to find optimal EEG channel selections for person identification. The proposed method (MOBCS-KNN) used a weighted sum technique to implement a multiobjective approach. In addition, a KNN classifier for EEG-based biometric person identification is used. It is worth mentioning that this is the initial investigation of using a multiobjective technique with EEG channel selection problem. A standard EEG motor imagery dataset is used to evaluate the performance of the MOBCS-KNN. The experiments show that the MOBCS-KNN obtained accuracy of 93.86% using only 24 sensors with AR20 autoregressive coefficients. Another critical point is that the MOBCS-KNN finds channels not too close to each other to capture relevant information from all over the head. In conclusion, the MOBCS-KNN algorithm achieves the best results compared with metaheuristic algorithms. Finally, the recommended approach can draw future directions to be applied to different research areas. |
| doi_str_mv | 10.1155/2022/5974634 |
| format | Article |
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Several studies defined the EEG with unique features, universality, and natural robustness to be used as a new track to prevent spoofing attacks. The EEG signals are a visual recording of the brain’s electrical activities, measured by placing electrodes (channels) in various scalp positions. However, traditional EEG-based systems lead to high complexity with many channels, and some channels have critical information for the identification system while others do not. Several studies have proposed a single objective to address the EEG channel for person identification. Unfortunately, these studies only focused on increasing the accuracy rate without balancing the accuracy and the total number of selected EEG channels. The novelty of this paper is to propose a multiobjective binary version of the cuckoo search algorithm (MOBCS-KNN) to find optimal EEG channel selections for person identification. The proposed method (MOBCS-KNN) used a weighted sum technique to implement a multiobjective approach. In addition, a KNN classifier for EEG-based biometric person identification is used. It is worth mentioning that this is the initial investigation of using a multiobjective technique with EEG channel selection problem. A standard EEG motor imagery dataset is used to evaluate the performance of the MOBCS-KNN. The experiments show that the MOBCS-KNN obtained accuracy of 93.86% using only 24 sensors with AR20 autoregressive coefficients. Another critical point is that the MOBCS-KNN finds channels not too close to each other to capture relevant information from all over the head. In conclusion, the MOBCS-KNN algorithm achieves the best results compared with metaheuristic algorithms. Finally, the recommended approach can draw future directions to be applied to different research areas.</description><identifier>ISSN: 1687-5265</identifier><identifier>EISSN: 1687-5273</identifier><identifier>DOI: 10.1155/2022/5974634</identifier><identifier>PMID: 35069721</identifier><language>eng</language><publisher>United States: Hindawi</publisher><subject>Accuracy ; Algorithms ; Biometrics ; Brain-Computer Interfaces ; Channels ; Classification ; Critical point ; Delivery of Health Care ; EEG ; Electrodes ; Electroencephalography ; Feature selection ; Genetic algorithms ; Heuristic methods ; Humans ; Identification systems ; Mental task performance ; Optimization algorithms ; Optimization techniques ; Search algorithms ; Sensors ; Spoofing ; Visual signals ; Voice recognition ; Wavelet transforms</subject><ispartof>Computational intelligence and neuroscience, 2022-01, Vol.2022, p.5974634-18</ispartof><rights>Copyright © 2022 Zaid Abdi Alkareem Alyasseri et al.</rights><rights>COPYRIGHT 2022 John Wiley & Sons, Inc.</rights><rights>Copyright © 2022 Zaid Abdi Alkareem Alyasseri et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><rights>Copyright © 2022 Zaid Abdi Alkareem Alyasseri et al. 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c542t-ab33b97b24dd881c1c2c3b3cd997d687f61d9d148b8ba2687ba9db179cffe28f3</citedby><cites>FETCH-LOGICAL-c542t-ab33b97b24dd881c1c2c3b3cd997d687f61d9d148b8ba2687ba9db179cffe28f3</cites><orcidid>0000-0001-9030-8102 ; 0000-0003-4228-9298 ; 0000-0002-7815-8946 ; 0000-0003-1980-1791 ; 0000-0003-3897-4460 ; 0000-0003-1936-6785</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8769868/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8769868/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27923,27924,53790,53792</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35069721$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Ding, Bai Yuan</contributor><contributor>Bai Yuan Ding</contributor><creatorcontrib>Abdi Alkareem Alyasseri, Zaid</creatorcontrib><creatorcontrib>Alomari, Osama Ahmad</creatorcontrib><creatorcontrib>Al-Betar, Mohammed Azmi</creatorcontrib><creatorcontrib>Awadallah, Mohammed A.</creatorcontrib><creatorcontrib>Hameed Abdulkareem, Karrar</creatorcontrib><creatorcontrib>Abed Mohammed, Mazin</creatorcontrib><creatorcontrib>Kadry, Seifedine</creatorcontrib><creatorcontrib>Rajinikanth, V.</creatorcontrib><creatorcontrib>Rho, Seungmin</creatorcontrib><title>EEG Channel Selection Using Multiobjective Cuckoo Search for Person Identification as Protection System in Healthcare Applications</title><title>Computational intelligence and neuroscience</title><addtitle>Comput Intell Neurosci</addtitle><description>Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. 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The proposed method (MOBCS-KNN) used a weighted sum technique to implement a multiobjective approach. In addition, a KNN classifier for EEG-based biometric person identification is used. It is worth mentioning that this is the initial investigation of using a multiobjective technique with EEG channel selection problem. A standard EEG motor imagery dataset is used to evaluate the performance of the MOBCS-KNN. The experiments show that the MOBCS-KNN obtained accuracy of 93.86% using only 24 sensors with AR20 autoregressive coefficients. Another critical point is that the MOBCS-KNN finds channels not too close to each other to capture relevant information from all over the head. In conclusion, the MOBCS-KNN algorithm achieves the best results compared with metaheuristic algorithms. 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Channel Selection Using Multiobjective Cuckoo Search for Person Identification as Protection System in Healthcare Applications</title><author>Abdi Alkareem Alyasseri, Zaid ; Alomari, Osama Ahmad ; Al-Betar, Mohammed Azmi ; Awadallah, Mohammed A. ; Hameed Abdulkareem, Karrar ; Abed Mohammed, Mazin ; Kadry, Seifedine ; Rajinikanth, V. ; Rho, Seungmin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c542t-ab33b97b24dd881c1c2c3b3cd997d687f61d9d148b8ba2687ba9db179cffe28f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Biometrics</topic><topic>Brain-Computer Interfaces</topic><topic>Channels</topic><topic>Classification</topic><topic>Critical point</topic><topic>Delivery of Health Care</topic><topic>EEG</topic><topic>Electrodes</topic><topic>Electroencephalography</topic><topic>Feature selection</topic><topic>Genetic 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Selection Using Multiobjective Cuckoo Search for Person Identification as Protection System in Healthcare Applications</atitle><jtitle>Computational intelligence and neuroscience</jtitle><addtitle>Comput Intell Neurosci</addtitle><date>2022-01-12</date><risdate>2022</risdate><volume>2022</volume><spage>5974634</spage><epage>18</epage><pages>5974634-18</pages><issn>1687-5265</issn><eissn>1687-5273</eissn><abstract>Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. Several studies defined the EEG with unique features, universality, and natural robustness to be used as a new track to prevent spoofing attacks. The EEG signals are a visual recording of the brain’s electrical activities, measured by placing electrodes (channels) in various scalp positions. However, traditional EEG-based systems lead to high complexity with many channels, and some channels have critical information for the identification system while others do not. Several studies have proposed a single objective to address the EEG channel for person identification. Unfortunately, these studies only focused on increasing the accuracy rate without balancing the accuracy and the total number of selected EEG channels. The novelty of this paper is to propose a multiobjective binary version of the cuckoo search algorithm (MOBCS-KNN) to find optimal EEG channel selections for person identification. The proposed method (MOBCS-KNN) used a weighted sum technique to implement a multiobjective approach. In addition, a KNN classifier for EEG-based biometric person identification is used. It is worth mentioning that this is the initial investigation of using a multiobjective technique with EEG channel selection problem. A standard EEG motor imagery dataset is used to evaluate the performance of the MOBCS-KNN. The experiments show that the MOBCS-KNN obtained accuracy of 93.86% using only 24 sensors with AR20 autoregressive coefficients. Another critical point is that the MOBCS-KNN finds channels not too close to each other to capture relevant information from all over the head. In conclusion, the MOBCS-KNN algorithm achieves the best results compared with metaheuristic algorithms. 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| fulltext | fulltext |
| identifier | ISSN: 1687-5265 |
| ispartof | Computational intelligence and neuroscience, 2022-01, Vol.2022, p.5974634-18 |
| issn | 1687-5265 1687-5273 |
| language | eng |
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| source | MEDLINE; PubMed Central Open Access; Wiley-Blackwell Open Access Titles; EZB-FREE-00999 freely available EZB journals; PubMed Central; Alma/SFX Local Collection |
| subjects | Accuracy Algorithms Biometrics Brain-Computer Interfaces Channels Classification Critical point Delivery of Health Care EEG Electrodes Electroencephalography Feature selection Genetic algorithms Heuristic methods Humans Identification systems Mental task performance Optimization algorithms Optimization techniques Search algorithms Sensors Spoofing Visual signals Voice recognition Wavelet transforms |
| title | EEG Channel Selection Using Multiobjective Cuckoo Search for Person Identification as Protection System in Healthcare Applications |
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