Multi-objective optimization for EEG channel selection and accurate intruder detection in an EEG-based subject identification system

• Published in: Scientific reports Vol. 10; no. 1; p. 5850
• Main Authors: Moctezuma, Luis Alfredo, Molinas, Marta
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 03.04.2020; Nature Publishing Group
• Subjects: 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
• DOI: 10.1038/s41598-020-62712-6

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.