Multi-objective optimization for EEG channel selection and accurate intruder detection in an EEG-based subject identification system
We present a four-objective optimization method for optimal electroencephalographic (EEG) channel selection to provide access to subjects with permission in a system by detecting intruders and identifying the subject. Each instance was represented by four features computed from two sub-bands, extrac...
Uloženo v:
| Vydáno v: | Scientific reports Ročník 10; číslo 1; s. 5850 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
London
Nature Publishing Group UK
03.04.2020
Nature Publishing Group |
| Témata: | |
| ISSN: | 2045-2322, 2045-2322 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | We present a four-objective optimization method for optimal electroencephalographic (EEG) channel selection to provide access to subjects with permission in a system by detecting intruders and identifying the subject. Each instance was represented by four features computed from two sub-bands, extracted using empirical mode decomposition (EMD) for each channel, and the feature vectors were used as input for one-class/multi-class support vector machines (SVMs). We tested the method on data from the event-related potentials (ERPs) of 26 subjects and 56 channels. The optimization process was performed by the non-dominated sorting genetic algorithm (NSGA), which found a three-channel combination that achieved an accuracy of 0.83, with both a true acceptance rate (TAR) and a true rejection rate (TRR) of 1.00. In the best case, we obtained an accuracy of up to 0.98 for subject identification with a TAR of 0.95 and a TRR 0.93, all using seven EEG channels found by NSGA-III in a subset of subjects manually created. The findings were also validated using 10 different subdivisions of subjects randomly created, obtaining up to 0.97 ± 0.02 of accuracy, a TAR of 0.81 ± 0.12 and TRR of 0.85 ± 0.10 using eight channels found by NSGA-III. These results support further studies on larger datasets for potential applications of EEG in identification and authentication systems. |
|---|---|
| AbstractList | We present a four-objective optimization method for optimal electroencephalographic (EEG) channel selection to provide access to subjects with permission in a system by detecting intruders and identifying the subject. Each instance was represented by four features computed from two sub-bands, extracted using empirical mode decomposition (EMD) for each channel, and the feature vectors were used as input for one-class/multi-class support vector machines (SVMs). We tested the method on data from the event-related potentials (ERPs) of 26 subjects and 56 channels. The optimization process was performed by the non-dominated sorting genetic algorithm (NSGA), which found a three-channel combination that achieved an accuracy of 0.83, with both a true acceptance rate (TAR) and a true rejection rate (TRR) of 1.00. In the best case, we obtained an accuracy of up to 0.98 for subject identification with a TAR of 0.95 and a TRR 0.93, all using seven EEG channels found by NSGA-III in a subset of subjects manually created. The findings were also validated using 10 different subdivisions of subjects randomly created, obtaining up to 0.97 ± 0.02 of accuracy, a TAR of 0.81 ± 0.12 and TRR of 0.85 ± 0.10 using eight channels found by NSGA-III. These results support further studies on larger datasets for potential applications of EEG in identification and authentication systems. We present a four-objective optimization method for optimal electroencephalographic (EEG) channel selection to provide access to subjects with permission in a system by detecting intruders and identifying the subject. Each instance was represented by four features computed from two sub-bands, extracted using empirical mode decomposition (EMD) for each channel, and the feature vectors were used as input for one-class/multi-class support vector machines (SVMs). We tested the method on data from the event-related potentials (ERPs) of 26 subjects and 56 channels. The optimization process was performed by the non-dominated sorting genetic algorithm (NSGA), which found a three-channel combination that achieved an accuracy of 0.83, with both a true acceptance rate (TAR) and a true rejection rate (TRR) of 1.00. In the best case, we obtained an accuracy of up to 0.98 for subject identification with a TAR of 0.95 and a TRR 0.93, all using seven EEG channels found by NSGA-III in a subset of subjects manually created. The findings were also validated using 10 different subdivisions of subjects randomly created, obtaining up to 0.97 ± 0.02 of accuracy, a TAR of 0.81 ± 0.12 and TRR of 0.85 ± 0.10 using eight channels found by NSGA-III. These results support further studies on larger datasets for potential applications of EEG in identification and authentication systems. We present a four-objective optimization method for optimal electroencephalographic (EEG) channel selection to provide access to subjects with permission in a system by detecting intruders and identifying the subject. Each instance was represented by four features computed from two sub-bands, extracted using empirical mode decomposition (EMD) for each channel, and the feature vectors were used as input for one-class/multi-class support vector machines (SVMs). We tested the method on data from the event-related potentials (ERPs) of 26 subjects and 56 channels. The optimization process was performed by the non-dominated sorting genetic algorithm (NSGA), which found a three-channel combination that achieved an accuracy of 0.83, with both a true acceptance rate (TAR) and a true rejection rate (TRR) of 1.00. In the best case, we obtained an accuracy of up to 0.98 for subject identification with a TAR of 0.95 and a TRR 0.93, all using seven EEG channels found by NSGA-III in a subset of subjects manually created. The findings were also validated using 10 different subdivisions of subjects randomly created, obtaining up to 0.97 ± 0.02 of accuracy, a TAR of 0.81 ± 0.12 and TRR of 0.85 ± 0.10 using eight channels found by NSGA-III. These results support further studies on larger datasets for potential applications of EEG in identification and authentication systems.We present a four-objective optimization method for optimal electroencephalographic (EEG) channel selection to provide access to subjects with permission in a system by detecting intruders and identifying the subject. Each instance was represented by four features computed from two sub-bands, extracted using empirical mode decomposition (EMD) for each channel, and the feature vectors were used as input for one-class/multi-class support vector machines (SVMs). We tested the method on data from the event-related potentials (ERPs) of 26 subjects and 56 channels. The optimization process was performed by the non-dominated sorting genetic algorithm (NSGA), which found a three-channel combination that achieved an accuracy of 0.83, with both a true acceptance rate (TAR) and a true rejection rate (TRR) of 1.00. In the best case, we obtained an accuracy of up to 0.98 for subject identification with a TAR of 0.95 and a TRR 0.93, all using seven EEG channels found by NSGA-III in a subset of subjects manually created. The findings were also validated using 10 different subdivisions of subjects randomly created, obtaining up to 0.97 ± 0.02 of accuracy, a TAR of 0.81 ± 0.12 and TRR of 0.85 ± 0.10 using eight channels found by NSGA-III. These results support further studies on larger datasets for potential applications of EEG in identification and authentication systems. |
| ArticleNumber | 5850 |
| Author | Molinas, Marta Moctezuma, Luis Alfredo |
| Author_xml | – sequence: 1 givenname: Luis Alfredo surname: Moctezuma fullname: Moctezuma, Luis Alfredo email: luis.a.moctezuma@ntnu.no organization: Department of Engineering Cybernetics, Norwegian University of Science and Technology – sequence: 2 givenname: Marta surname: Molinas fullname: Molinas, Marta organization: Department of Engineering Cybernetics, Norwegian University of Science and Technology |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32246122$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kkFvFCEUx4mpsbX2C3gwJF68jDLAMHAxMc1aTWq86Jkw8KZlMwsrME3asx9cdmertYdygfB-__f-PN5LdBRiAIRet-R9S5j8kHnbKdkQShpB-5Y24hk6oYR3DWWUHj04H6OznNekro4q3qoX6LjectFSeoJ-f5un4ps4rMEWfwM4bovf-DtTfAx4jAmvVhfYXpsQYMIZph1WIyY4bKydkymAfShpdpCwg3KI-x2y0zaDyeBwnvcVsHcQih-9XQrk21xg8wo9H82U4eywn6Kfn1c_zr80l98vvp5_umxsx0lpHOVuMNwp0ffOGqekHBWowXIhGHVUKACpWNe33LaD6UfJu06w3gEfHBMDO0Ufl7zbediAs9VKMpPeJr8x6VZH4_X_keCv9VW80bW_HVGsJnh3SJDirxly0RufLUyTCRDnrCmTgkolSV_Rt4_QdZxTqM_bU9Vl16tKvXno6K-V-w-qgFwAm2LOCUZtfdn3rhr0k26J3o2DXsZB13HQ-3HQokrpI-l99idFbBHlCocrSP9sP6H6A-plycE |
| CitedBy_id | crossref_primary_10_1088_1741_2552_ac0489 crossref_primary_10_1038_s41598_020_72051_1 crossref_primary_10_1038_s41598_022_18502_3 crossref_primary_10_1109_ACCESS_2025_3539502 crossref_primary_10_3390_s23094239 crossref_primary_10_3390_s23010186 crossref_primary_10_1109_JSEN_2023_3313236 crossref_primary_10_1007_s42979_022_01260_4 crossref_primary_10_1109_TIM_2025_3600822 crossref_primary_10_3390_biomimetics8040378 crossref_primary_10_1016_j_bspc_2023_104783 crossref_primary_10_1007_s13369_023_07798_6 crossref_primary_10_1038_s41598_024_68978_4 crossref_primary_10_3390_math10132266 crossref_primary_10_1109_TITS_2024_3442249 crossref_primary_10_1038_s41598_022_15252_0 crossref_primary_10_1016_j_asoc_2023_110496 crossref_primary_10_3389_fnins_2020_00593 crossref_primary_10_1109_ACCESS_2021_3092840 crossref_primary_10_1016_j_heliyon_2023_e15258 crossref_primary_10_1109_ACCESS_2023_3264266 crossref_primary_10_1155_2022_5974634 |
| Cites_doi | 10.1016/0167-2789(88)90081-4 10.1016/j.eswa.2018.10.004 10.1109/ICASSP.1999.758115 10.1109/BTAS.2010.5634515 10.1016/j.eswa.2019.01.080 10.1109/4235.996017 10.1007/978-3-030-12385-7_57 10.12928/telkomnika.v14i4.3956 10.1007/s00500-017-2965-0 10.1109/TEVC.2013.2281534 10.1109/CCST.2012.6393564 10.1109/INDIN41052.2019.8972231 10.1109/TEVC.2013.2281535 10.1109/APSCC.2011.87 10.1016/0013-4694(88)90149-6 10.1109/ISABEL.2010.5702895 10.1098/rspa.1998.0193 10.1162/evco.1994.2.3.221 10.1137/S1052623496307510 10.1109/ICIP.2015.7351055 10.1007/978-3-030-05587-5_43 10.1145/3230632 10.1155/2012/578295 10.5120/ijca2015906480 10.1109/ACCESS.2019.2907644 10.1016/j.csl.2009.05.003 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2020 The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s) 2020 – notice: The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7X7 7XB 88A 88E 88I 8FE 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M2P M7P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI Q9U 7X8 5PM |
| DOI | 10.1038/s41598-020-62712-6 |
| DatabaseName | Springer Nature OA Free Journals (WRLC) CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Biology Database (Alumni Edition) Medical Database (Alumni Edition) Science Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Collection ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central ProQuest One Sustainability ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection ProQuest One ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Biological Sciences ProQuest Health & Medical Collection Medical Database Science Database Biological Science Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Biology Journals (Alumni Edition) ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest Central Basic ProQuest Science Journals ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE Publicly Available Content Database MEDLINE - Academic CrossRef |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 2045-2322 |
| ExternalDocumentID | PMC7125093 32246122 10_1038_s41598_020_62712_6 |
| Genre | Research Support, Non-U.S. Gov't Journal Article |
| GroupedDBID | 0R~ 3V. 4.4 53G 5VS 7X7 88A 88E 88I 8FE 8FH 8FI 8FJ AAFWJ AAJSJ AAKDD ABDBF ABUWG ACGFS ACSMW ACUHS ADBBV ADRAZ AENEX AEUYN AFKRA AJTQC ALIPV ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BAWUL BBNVY BCNDV BENPR BHPHI BPHCQ BVXVI C6C CCPQU DIK DWQXO EBD EBLON EBS ESX FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE KQ8 LK8 M0L M1P M2P M48 M7P M~E NAO OK1 PIMPY PQQKQ PROAC PSQYO RNT RNTTT RPM SNYQT UKHRP AASML AAYXX AFFHD AFPKN CITATION PHGZM PHGZT PJZUB PPXIY PQGLB CGR CUY CVF ECM EIF NPM 7XB 8FK K9. PKEHL PQEST PQUKI Q9U 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c540t-d24dba4d9677dcad988f9e9bc46632d269ee8935714c1ba7f8455637de4bd36b3 |
| IEDL.DBID | M7P |
| ISICitedReferencesCount | 27 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000563485200016&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2045-2322 |
| IngestDate | Tue Nov 04 01:35:37 EST 2025 Fri Sep 05 10:10:53 EDT 2025 Tue Oct 07 07:21:05 EDT 2025 Thu Apr 03 07:10:00 EDT 2025 Sat Nov 29 05:32:29 EST 2025 Tue Nov 18 22:20:40 EST 2025 Fri Feb 21 02:36:56 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| License | Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c540t-d24dba4d9677dcad988f9e9bc46632d269ee8935714c1ba7f8455637de4bd36b3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | https://www.proquest.com/docview/2386357579?pq-origsite=%requestingapplication% |
| PMID | 32246122 |
| PQID | 2386357579 |
| PQPubID | 2041939 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_7125093 proquest_miscellaneous_2386289807 proquest_journals_2386357579 pubmed_primary_32246122 crossref_citationtrail_10_1038_s41598_020_62712_6 crossref_primary_10_1038_s41598_020_62712_6 springer_journals_10_1038_s41598_020_62712_6 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-04-03 |
| PublicationDateYYYYMMDD | 2020-04-03 |
| PublicationDate_xml | – month: 04 year: 2020 text: 2020-04-03 day: 03 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | Scientific reports |
| PublicationTitleAbbrev | Sci Rep |
| PublicationTitleAlternate | Sci Rep |
| PublicationYear | 2020 |
| Publisher | Nature Publishing Group UK Nature Publishing Group |
| Publisher_xml | – name: Nature Publishing Group UK – name: Nature Publishing Group |
| References | Moctezuma, L. A. & Molinas, M. Event-related potential from eeg for a two-step identity authentication system. In IEEE 17th International Conference on Industrial Informatics (INDIN) (IEEE, 2019). DebKJainHAn evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraintsIEEE Transactions on Evolutionary Computation20131857760110.1109/TEVC.2013.2281535 FabianiMDefinition, identification, and reliability of measurement of the p300 component of the event-related brain potentialAdvances in psychophysiology1987278 ChughTSindhyaKHakanenJMiettinenKA survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithmsSoft Computing2019233137316610.1007/s00500-017-2965-0 JainHDebKAn evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, part ii: handling constraints and extending to an adaptive approachIEEE Transactions on Evolutionary Computation20131860262210.1109/TEVC.2013.2281534 Abdiansah, A. & Wardoyo, R. Time complexity analysis of support vector machines (svm) in libsvm. International journal computer and application (2015). DebKPratapAAgarwalSMeyarivanTA fast and elitist multiobjective genetic algorithm: Nsga-iiIEEE transactions on evolutionary computation2002618219710.1109/4235.996017 Davis, P., Creusere, C. D. & Kroger, J. Subject identification based on eeg responses to video stimuli. In 2015 IEEE International Conference on Image Processing (ICIP), 1523–1527 (IEEE, 2015). Boutana, D., Benidir, M. & Barkat, B. On the selection of intrinsic mode function in emd method: application on heart sound signal. In 2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL 2010), 1–5 (IEEE, 2010). SunYLoFP-WLoBEeg-based user identification system using 1d-convolutional long short-term memory neural networksExpert Systems with Applications201912525926710.1016/j.eswa.2019.01.080 Joachims, T. Making large-scale svm learning practical. Tech. Rep., Technical report, SFB 475: Komplexitätsreduktion in Multivariaten (1998). SrinivasNDebKMuiltiobjective optimization using nondominated sorting in genetic algorithmsEvolutionary computation1994222124810.1162/evco.1994.2.3.221 Jabloun, F. & Cetin, A. E. The teager energy based feature parameters for robust speech recognition in car noise. In 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No. 99CH36258), vol. 1, 273–276 (IEEE, 1999). Petrosian, A. Kolmogorov complexity of finite sequences and recognition of different preictal eeg patterns. In Proceedings Eighth IEEE Symposium on Computer-Based Medical Systems, 212–217 (IEEE, 1995). SyarifIPrugel-BennettAWillsGSvm parameter optimization using grid search and genetic algorithm to improve classification performanceTelkomnika201614150210.12928/telkomnika.v14i4.3956 GuiQRuiz-BlondetMVLaszloSJinZA survey on brain biometricsACM Comput. Surv.201951112:1112:3810.1145/3230632 DasIDennisJENormal-boundary intersection: A new method for generating the pareto surface in nonlinear multicriteria optimization problemsSIAM journal on optimization19988631657162715610.1137/S1052623496307510 Moctezuma, L. A. & Molinas, M. Eeg-based subjects identification based on biometrics of imagined speech using emd. In International Conference on Brain Informatics, 458–467 (Springer, 2018). RieraASoria-FrischACaparriniMGrauCRuffiniGUnobtrusive biometric system based on electroencephalogram analysisEURASIP Journal on Advances in Signal Processing200820081184.94130 Chen, J., Mao, Z., Yao, W. & Huang, Y. Eeg-based biometric identification with convolutional neural network. Multimedia Tools and Applications 1–21 (2019). HuangNEThe empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysisProceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences19984549039951998RSPSA.454..903H163159110.1098/rspa.1998.0193 MargauxPEmmanuelMSébastienDOlivierBJérémieMObjective and subjective evaluation of online error correction during p300-based spellingAdvances in Human-Computer Interaction20122012410.1155/2012/578295 Safont, G., Salazar, A., Soriano, A. & Vergara, L. Combination of multiple detectors for eeg based biometric identification/authentication. In 2012 IEEE International Carnahan Conference on Security Technology (ICCST), 230–236 (IEEE, 2012). MoctezumaLATorres-GarcíaAAVillaseñor-PinedaLCarrilloMSubjects identification using eeg-recorded imagined speechExpert Systems with Applications201911820120810.1016/j.eswa.2018.10.004 FarwellLADonchinETalking off the top of your head: toward a mental prosthesis utilizing event-related brain potentialsElectroencephalography and clinical Neurophysiology1988705105231:STN:280:DyaL1M%2Flt1GktA%3D%3D10.1016/0013-4694(88)90149-6 DidiotEIllinaIFohrDMellaOA wavelet-based parameterization for speech/music discriminationComputer Speech & Language20102434135710.1016/j.csl.2009.05.003 HiguchiTApproach to an irregular time series on the basis of the fractal theoryPhysica D: Nonlinear Phenomena1988312772831988PhyD...31..277H95563210.1016/0167-2789(88)90081-4 Brigham, K. & Kumar, B. V. Subject identification from electroencephalogram (eeg) signals during imagined speech. In 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), 1–8 (IEEE, 2010). Moctezuma, L. A. & Molinas, M. Subject identification from low-density eeg-recordings of resting-states: A study of feature extraction and classification. In Future of Information and Communication Conference, 830–846 (Springer, 2019). DiYRobustness analysis of identification using resting-state eeg signalsIEEE Access20197421134212210.1109/ACCESS.2019.2907644 Hu, B., Liu, Q., Zhao, Q., Qi, Y. & Peng, H. A real-time electroencephalogram (eeg) based individual identification interface for mobile security in ubiquitous environment. In 2011 IEEE Asia-Pacific Services Computing Conference, 436–441 (IEEE, 2011). 62712_CR23 Q Gui (62712_CR9) 2019; 51 62712_CR21 62712_CR26 T Chugh (62712_CR27) 2019; 23 NE Huang (62712_CR18) 1998; 454 62712_CR25 62712_CR1 I Syarif (62712_CR24) 2016; 14 T Higuchi (62712_CR22) 1988; 31 H Jain (62712_CR30) 2013; 18 62712_CR19 E Didiot (62712_CR20) 2010; 24 Y Sun (62712_CR15) 2019; 125 62712_CR11 62712_CR4 LA Farwell (62712_CR7) 1988; 70 62712_CR12 P Margaux (62712_CR17) 2012; 2012 62712_CR2 I Das (62712_CR31) 1998; 8 A Riera (62712_CR10) 2008; 2008 62712_CR13 N Srinivas (62712_CR16) 1994; 2 62712_CR6 62712_CR14 K Deb (62712_CR29) 2013; 18 Y Di (62712_CR5) 2019; 7 M Fabiani (62712_CR8) 1987; 2 LA Moctezuma (62712_CR3) 2019; 118 K Deb (62712_CR28) 2002; 6 |
| References_xml | – reference: Moctezuma, L. A. & Molinas, M. Subject identification from low-density eeg-recordings of resting-states: A study of feature extraction and classification. In Future of Information and Communication Conference, 830–846 (Springer, 2019). – reference: Jabloun, F. & Cetin, A. E. The teager energy based feature parameters for robust speech recognition in car noise. In 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No. 99CH36258), vol. 1, 273–276 (IEEE, 1999). – reference: DidiotEIllinaIFohrDMellaOA wavelet-based parameterization for speech/music discriminationComputer Speech & Language20102434135710.1016/j.csl.2009.05.003 – reference: JainHDebKAn evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, part ii: handling constraints and extending to an adaptive approachIEEE Transactions on Evolutionary Computation20131860262210.1109/TEVC.2013.2281534 – reference: Davis, P., Creusere, C. D. & Kroger, J. Subject identification based on eeg responses to video stimuli. In 2015 IEEE International Conference on Image Processing (ICIP), 1523–1527 (IEEE, 2015). – reference: SrinivasNDebKMuiltiobjective optimization using nondominated sorting in genetic algorithmsEvolutionary computation1994222124810.1162/evco.1994.2.3.221 – reference: Boutana, D., Benidir, M. & Barkat, B. On the selection of intrinsic mode function in emd method: application on heart sound signal. In 2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL 2010), 1–5 (IEEE, 2010). – reference: DiYRobustness analysis of identification using resting-state eeg signalsIEEE Access20197421134212210.1109/ACCESS.2019.2907644 – reference: MargauxPEmmanuelMSébastienDOlivierBJérémieMObjective and subjective evaluation of online error correction during p300-based spellingAdvances in Human-Computer Interaction20122012410.1155/2012/578295 – reference: HiguchiTApproach to an irregular time series on the basis of the fractal theoryPhysica D: Nonlinear Phenomena1988312772831988PhyD...31..277H95563210.1016/0167-2789(88)90081-4 – reference: Abdiansah, A. & Wardoyo, R. Time complexity analysis of support vector machines (svm) in libsvm. International journal computer and application (2015). – reference: DebKJainHAn evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraintsIEEE Transactions on Evolutionary Computation20131857760110.1109/TEVC.2013.2281535 – reference: MoctezumaLATorres-GarcíaAAVillaseñor-PinedaLCarrilloMSubjects identification using eeg-recorded imagined speechExpert Systems with Applications201911820120810.1016/j.eswa.2018.10.004 – reference: GuiQRuiz-BlondetMVLaszloSJinZA survey on brain biometricsACM Comput. Surv.201951112:1112:3810.1145/3230632 – reference: FarwellLADonchinETalking off the top of your head: toward a mental prosthesis utilizing event-related brain potentialsElectroencephalography and clinical Neurophysiology1988705105231:STN:280:DyaL1M%2Flt1GktA%3D%3D10.1016/0013-4694(88)90149-6 – reference: SunYLoFP-WLoBEeg-based user identification system using 1d-convolutional long short-term memory neural networksExpert Systems with Applications201912525926710.1016/j.eswa.2019.01.080 – reference: HuangNEThe empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysisProceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences19984549039951998RSPSA.454..903H163159110.1098/rspa.1998.0193 – reference: Joachims, T. Making large-scale svm learning practical. Tech. Rep., Technical report, SFB 475: Komplexitätsreduktion in Multivariaten (1998). – reference: SyarifIPrugel-BennettAWillsGSvm parameter optimization using grid search and genetic algorithm to improve classification performanceTelkomnika201614150210.12928/telkomnika.v14i4.3956 – reference: FabianiMDefinition, identification, and reliability of measurement of the p300 component of the event-related brain potentialAdvances in psychophysiology1987278 – reference: Hu, B., Liu, Q., Zhao, Q., Qi, Y. & Peng, H. A real-time electroencephalogram (eeg) based individual identification interface for mobile security in ubiquitous environment. In 2011 IEEE Asia-Pacific Services Computing Conference, 436–441 (IEEE, 2011). – reference: Chen, J., Mao, Z., Yao, W. & Huang, Y. Eeg-based biometric identification with convolutional neural network. Multimedia Tools and Applications 1–21 (2019). – reference: DasIDennisJENormal-boundary intersection: A new method for generating the pareto surface in nonlinear multicriteria optimization problemsSIAM journal on optimization19988631657162715610.1137/S1052623496307510 – reference: Moctezuma, L. A. & Molinas, M. Event-related potential from eeg for a two-step identity authentication system. In IEEE 17th International Conference on Industrial Informatics (INDIN) (IEEE, 2019). – reference: RieraASoria-FrischACaparriniMGrauCRuffiniGUnobtrusive biometric system based on electroencephalogram analysisEURASIP Journal on Advances in Signal Processing200820081184.94130 – reference: DebKPratapAAgarwalSMeyarivanTA fast and elitist multiobjective genetic algorithm: Nsga-iiIEEE transactions on evolutionary computation2002618219710.1109/4235.996017 – reference: Petrosian, A. Kolmogorov complexity of finite sequences and recognition of different preictal eeg patterns. In Proceedings Eighth IEEE Symposium on Computer-Based Medical Systems, 212–217 (IEEE, 1995). – reference: ChughTSindhyaKHakanenJMiettinenKA survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithmsSoft Computing2019233137316610.1007/s00500-017-2965-0 – reference: Moctezuma, L. A. & Molinas, M. Eeg-based subjects identification based on biometrics of imagined speech using emd. In International Conference on Brain Informatics, 458–467 (Springer, 2018). – reference: Safont, G., Salazar, A., Soriano, A. & Vergara, L. Combination of multiple detectors for eeg based biometric identification/authentication. In 2012 IEEE International Carnahan Conference on Security Technology (ICCST), 230–236 (IEEE, 2012). – reference: Brigham, K. & Kumar, B. V. Subject identification from electroencephalogram (eeg) signals during imagined speech. In 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), 1–8 (IEEE, 2010). – ident: 62712_CR25 – ident: 62712_CR23 – volume: 31 start-page: 277 year: 1988 ident: 62712_CR22 publication-title: Physica D: Nonlinear Phenomena doi: 10.1016/0167-2789(88)90081-4 – volume: 118 start-page: 201 year: 2019 ident: 62712_CR3 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2018.10.004 – ident: 62712_CR21 doi: 10.1109/ICASSP.1999.758115 – ident: 62712_CR1 doi: 10.1109/BTAS.2010.5634515 – volume: 125 start-page: 259 year: 2019 ident: 62712_CR15 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2019.01.080 – volume: 6 start-page: 182 year: 2002 ident: 62712_CR28 publication-title: IEEE transactions on evolutionary computation doi: 10.1109/4235.996017 – ident: 62712_CR4 doi: 10.1007/978-3-030-12385-7_57 – volume: 14 start-page: 1502 year: 2016 ident: 62712_CR24 publication-title: Telkomnika doi: 10.12928/telkomnika.v14i4.3956 – volume: 23 start-page: 3137 year: 2019 ident: 62712_CR27 publication-title: Soft Computing doi: 10.1007/s00500-017-2965-0 – volume: 18 start-page: 602 year: 2013 ident: 62712_CR30 publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2013.2281534 – ident: 62712_CR11 doi: 10.1109/CCST.2012.6393564 – volume: 2008 year: 2008 ident: 62712_CR10 publication-title: EURASIP Journal on Advances in Signal Processing – ident: 62712_CR6 doi: 10.1109/INDIN41052.2019.8972231 – volume: 18 start-page: 577 year: 2013 ident: 62712_CR29 publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2013.2281535 – ident: 62712_CR12 doi: 10.1109/APSCC.2011.87 – volume: 70 start-page: 510 year: 1988 ident: 62712_CR7 publication-title: Electroencephalography and clinical Neurophysiology doi: 10.1016/0013-4694(88)90149-6 – ident: 62712_CR19 doi: 10.1109/ISABEL.2010.5702895 – volume: 454 start-page: 903 year: 1998 ident: 62712_CR18 publication-title: Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences doi: 10.1098/rspa.1998.0193 – volume: 2 start-page: 221 year: 1994 ident: 62712_CR16 publication-title: Evolutionary computation doi: 10.1162/evco.1994.2.3.221 – ident: 62712_CR14 – volume: 8 start-page: 631 year: 1998 ident: 62712_CR31 publication-title: SIAM journal on optimization doi: 10.1137/S1052623496307510 – volume: 2 start-page: 78 year: 1987 ident: 62712_CR8 publication-title: Advances in psychophysiology – ident: 62712_CR13 doi: 10.1109/ICIP.2015.7351055 – ident: 62712_CR2 doi: 10.1007/978-3-030-05587-5_43 – volume: 51 start-page: 112:1 year: 2019 ident: 62712_CR9 publication-title: ACM Comput. Surv. doi: 10.1145/3230632 – volume: 2012 start-page: 4 year: 2012 ident: 62712_CR17 publication-title: Advances in Human-Computer Interaction doi: 10.1155/2012/578295 – ident: 62712_CR26 doi: 10.5120/ijca2015906480 – volume: 7 start-page: 42113 year: 2019 ident: 62712_CR5 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2907644 – volume: 24 start-page: 341 year: 2010 ident: 62712_CR20 publication-title: Computer Speech & Language doi: 10.1016/j.csl.2009.05.003 |
| SSID | ssj0000529419 |
| Score | 2.4519181 |
| Snippet | We present a four-objective optimization method for optimal electroencephalographic (EEG) channel selection to provide access to subjects with permission in a... |
| SourceID | pubmedcentral proquest pubmed crossref springer |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 5850 |
| SubjectTerms | 631/114/116 631/114/1305 631/114/1314 631/114/2397 631/378/116 631/61/350/59 639/166/985 639/705/1041 692/53 Accuracy Adult Algorithms Brain - physiology EEG Electroencephalography - methods Event-related potentials Evoked Potentials - physiology Humanities and Social Sciences Humans multidisciplinary Optimization Patient Identification Systems - methods Reproducibility of Results Science Science (multidisciplinary) Support Vector Machine |
| Title | Multi-objective optimization for EEG channel selection and accurate intruder detection in an EEG-based subject identification system |
| URI | https://link.springer.com/article/10.1038/s41598-020-62712-6 https://www.ncbi.nlm.nih.gov/pubmed/32246122 https://www.proquest.com/docview/2386357579 https://www.proquest.com/docview/2386289807 https://pubmed.ncbi.nlm.nih.gov/PMC7125093 |
| Volume | 10 |
| WOSCitedRecordID | wos000563485200016&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: DOA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M~E dateStart: 20110101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: 7X7 dateStart: 20110101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Biological Science customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M7P dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: BENPR dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: PIMPY dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVPQU databaseName: Science Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M2P dateStart: 20110101 isFulltext: true titleUrlDefault: https://search.proquest.com/sciencejournals providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB7RFqReeD8CZWUkbmB143jj-IQAbYFDVxECaTlFju1Vg0rSNrtI3PnhzDjerZaKXrjkkLHlWDOe-eJ5Abys5QKNopNc2zTnEgEy134ieYHo2HiZL_zYhGYTajYr5nNdxgu3PoZVrnViUNSus3RHfoimhUqnTZR-c3bOqWsUeVdjC40d2KMqCVkI3Ss3dyzkxZKpjrky46w47NFeUU4Z_TMJlQqeb9ujKyDzaqzkXw7TYIeO7vzvDu7C7YhA2dtBZO7BDd_eh1tDT8pfD-B3SMnlXf19UIWsQ6XyI2ZrMoS4bDr9wChfuPWnrA9tdIhiWseMtSsqPcEa3MXK-Qvm_DLSGxpCczkZTsf6VViBNS7GKw0LDJWlH8LXo-mX9x95bNXALUK-JXdCutpIp3OlnDVOF8VCe11b5H4mnMi194iMJiqVNq2NWhSSKpMp52XtsrzOHsFu27X-CTArDUJpfJWjzAhlDMFOaxAY1qhepE0gXTOssrGOObXTOK2CPz0rqoHJFTK5Ckyu8gRebeacDVU8rh19sGZgFU90X11yL4EXGzKeRXKwmNZ3q2EM_sAWY5XA40FsNstloXKfEAmoLYHaDKA639uUtjkJ9b7xoxDWZQm8Xove5Wf9exdPr9_FM9gXdAwoCCk7gF0UC_8cbtqfy6a_GMGOmqvwLEaw9246Kz-PwnUFPo9FOQrnDCnlp-Py2x9Twy2_ |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VLQguvB-BAkaCE1jdON44PiCEYEtXbVd7KFI5Bcf2iqCSlGYX1Du_h9_ITF7VUtFbD1xjJ3acb2Y-x_MAeJ7JORpFJ7m2YcwlEmSu_UjyBNmx8TKe-6Gpi02o6TQ5ONCzNfjdxcKQW2WnE2tF7UpL_8g30bRQ6rSR0m-OvnOqGkWnq10JjQYWO_7kJ27ZqteT9_h9XwixNd5_t83bqgLcIjtZcCeky4x0OlbKWeN0ksy115nFiUbCiVh7j0Z8pEJpw8yoeSIpiZZyXmYuirMIn3sJ1iWCPRnA-myyN_vU_9WhczMZ6jY6ZxglmxVaSIpio12aUKHg8aoFPENrz3pn_nVEW1u-rRv_25rdhOstx2ZvG6G4BWu-uA1XmqqbJ3fgVx10zMvsa6PsWYlq81sbj8qQxLPx-AOjiOjCH7KqLhRELaZwzFi7pOQaLMdVWzp_zJxftO05daF7OVEDx6plPQLLXeuR1QzQ5M6-Cx8vZAnuwaAoC_8AmJUGNwt4KUapEMoYItbWIPXNUIFKG0DYASS1baZ2KhhymNYeA1GSNqBKEVRpDao0DuBlf89Rk6fk3N4bHWDSVmdV6SlaAnjWN6O2oSMkU_hy2fTBLXoyVAHcb2DaDxfVuQmFCECtALjvQJnMV1uK_Eud0RwnhcQ1CuBVB_XTaf37LR6e_xZP4er2_t5uujuZ7jyCa4JEkFyuog0YIET8Y7hsfyzy6vhJK8UMPl-0EPwBnuWF3w |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VAhUX3o9AASPBCazdON44OSCE6C5URas9gNRbcGxHpCpJaXZBvfOr-HXMOI9qqeitB66xEzvONw_HM98APM9lgUbRSp6aMOYSHWSeuonkCXrH2sm4cGPti02o-TzZ308XG_C7z4WhsMpeJ3pFbWtD_8hHaFqIOm2i0lHRhUUsdmZvjr5zqiBFJ619OY0WInvu5Cdu35rXuzv4rV8IMZt-eveBdxUGuEFPZcmtkDbX0qaxUtZomyZJkbo0NzjpSFgRp86hQZ-oUJow16pIJBFqKetkbqM4j_C5l-CyItJyHza4GP7v0AmaDNMuT2ccJaMGbSXls9F-TahQ8HjdFp5xcM_Gaf51WOtt4OzG_7x6N-F653mzt62o3IINV92Gq20tzpM78MunIvM6P2hNAKtRmX7rslQZuvZsOn3PKE-6coes8eWDqEVXlmljVkS5wUpcwZV1x8y6ZddeUhe6l5PDYFmz8iOw0nZxWu0ALaP2Xfh8IUtwDzarunIPgBmpcQuBl2KUFaG0JnfbaHSIc1Sr0gQQ9mDJTMffTmVEDjMfRxAlWQuwDAGWeYBlcQAvh3uOWvaSc3tv9-DJOk3WZKfICeDZ0Iw6iA6WdOXqVdsHN-7JWAVwv4XsMFzkGQuFCECtgXnoQPzm6y1V-dXznOOk0J2NAnjVw_50Wv9-i4fnv8VT2ELkZx9353uP4JogaaQ4rGgbNhEh7jFcMT-WZXP8xIszgy8XLQF_AMC2jR4 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Multi-objective+optimization+for+EEG+channel+selection+and+accurate+intruder+detection+in+an+EEG-based+subject+identification+system&rft.jtitle=Scientific+reports&rft.au=Moctezuma%2C+Luis+Alfredo&rft.au=Molinas%2C+Marta&rft.date=2020-04-03&rft.eissn=2045-2322&rft.volume=10&rft.issue=1&rft.spage=5850&rft_id=info:doi/10.1038%2Fs41598-020-62712-6&rft_id=info%3Apmid%2F32246122&rft.externalDocID=32246122 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2045-2322&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2045-2322&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2045-2322&client=summon |