dc.contributor.author | Moctezuma, Luis Alfredo | |
dc.contributor.author | Molinas Cabrera, Maria Marta | |
dc.date.accessioned | 2020-04-06T07:07:30Z | |
dc.date.available | 2020-04-06T07:07:30Z | |
dc.date.created | 2020-04-03T10:48:32Z | |
dc.date.issued | 2020 | |
dc.identifier.issn | 2045-2322 | |
dc.identifier.uri | https://hdl.handle.net/11250/2650434 | |
dc.description.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. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Nature Research | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Multi-objective optimization for EEG channel selection and accurate intruder detection in an EEG-based subject identification system | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.journal | Scientific Reports | en_US |
dc.identifier.doi | 10.1038/s41598-020-62712-6 | |
dc.identifier.cristin | 1805158 | |
dc.description.localcode | © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License | en_US |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |