dc.contributor.author | Moctezuma, Luis Alfredo | |
dc.contributor.author | Molinas Cabrera, Maria Marta | |
dc.date.accessioned | 2020-05-15T07:46:53Z | |
dc.date.available | 2020-05-15T07:46:53Z | |
dc.date.created | 2020-02-27T12:20:34Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | IEEE Conference on Industrial Informatics. 2019, 2019-July 392-399. | en_US |
dc.identifier.issn | 1935-4576 | |
dc.identifier.uri | https://hdl.handle.net/11250/2654556 | |
dc.description.abstract | Current problems related to high-level security access are increasing, leaving organizations and persons unsafe. A recent good candidate to create a robust identity authentication system is based on brain signals recorded with electroencephalograms (EEG). In this paper, EEG-based brain signals of 56 channels, from event-related potentials (ERPs), are used for Subject identification. The ERPs are from positive or negative feedback-related responses of a P300-speller system. The feature extraction part was done with empirical mode decomposition (EMD) extracting 2 intrinsic mode functions (IMFs) per channel, that were selected based on the Minkowski distance. After that, 4 features are computed per IMF; 2 energy features (instantaneous and teager energy) and 2 fractal features (Higuchi and Petrosian fractal dimension). Support vector machine (SVM) was used for the classification stage with an accuracy index computed using 10-folds cross-validation for evaluating the classifier's performance. Since high-density EEG information was available, the well-known backward-elimination and forward-addition greedy algorithms were used to reduce or increase the number of channels, step by step. Using the proposed method for subject identification from a positive or negative feedback-related response and then identify the subject will add a layer to improve the security system. The results obtained show that subject identification is feasible even using a low number of channels: E.g., 0.89 of accuracy using 5 channels with a mixed population and 0.93 with a male-only population. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.title | Event-related potential from EEG for a two-step Identity Authentication System | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | acceptedVersion | en_US |
dc.source.pagenumber | 392-399 | en_US |
dc.source.volume | 2019-July | en_US |
dc.source.journal | IEEE Conference on Industrial Informatics | en_US |
dc.identifier.doi | 10.1109/INDIN41052.2019.8972231 | |
dc.identifier.cristin | 1798112 | |
dc.description.localcode | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |