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...

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Vydáno v:Scientific reports Ročník 10; číslo 1; s. 5850
Hlavní autoři: Moctezuma, Luis Alfredo, Molinas, Marta
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 03.04.2020
Nature Publishing Group
Témata:
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
ISSN:2045-2322, 2045-2322
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Shrnutí: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-020-62712-6