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dc.contributor.authorMoctezuma, Luis Alfredo
dc.contributor.authorMolinas Cabrera, Maria Marta
dc.date.accessioned2020-04-06T07:07:30Z
dc.date.available2020-04-06T07:07:30Z
dc.date.created2020-04-03T10:48:32Z
dc.date.issued2020
dc.identifier.issn2045-2322
dc.identifier.urihttps://hdl.handle.net/11250/2650434
dc.description.abstractWe 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.en_US
dc.language.isoengen_US
dc.publisherNature Researchen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleMulti-objective optimization for EEG channel selection and accurate intruder detection in an EEG-based subject identification systemen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.journalScientific Reportsen_US
dc.identifier.doi10.1038/s41598-020-62712-6
dc.identifier.cristin1805158
dc.description.localcode© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International Licenseen_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal