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dc.contributor.authorTajDini, M
dc.contributor.authorSokolov, V
dc.contributor.authorKuzminykh, I
dc.contributor.authorGhita, B
dc.date.accessioned2023-04-17T10:20:30Z
dc.date.available2023-04-17T10:20:30Z
dc.date.issued2023-03-22
dc.identifier.issn0167-4048
dc.identifier.issn1872-6208
dc.identifier.other103198
dc.identifier.urihttps://pearl.plymouth.ac.uk/handle/10026.1/20700
dc.description.abstract

This article investigates the use of human brainwaves for user authentication. We used data collected from 50 volunteers and leveraged the Support Vector Machine (SVM) as a classification algorithm for the case study. User recognition patterns are taken from a combination of blinking, attention concentration, and picture recognition emotion sequences. These actions impact alpha, beta, gamma, and theta brain waves, which are measured using several electrodes. Ten different electrode placement patterns are explored, with varied positioning on the head. For each placement position, four features are examined, for a total of 40 extracts in the learning model. Features are: 1) spectral information, 2) coherence, 3) mutual correlation coefficient, and 4) mutual information. Each feature type is trained by the SVM algorithm, and the 40 weak classifier candidates. Adaptive Boosting (AdaBoost), a type of machine learning, is then used to generate a robust classifier, which is subsequently used to create a model, and select features, used to accurately identify individuals for authentication purposes. Upon verifying the proposed method using 32 legitimate users and 18 intruders, we obtained an authentication error rate (ERR) of 0.52%, and a classification rate of 99.06%.

dc.format.extent103198-103198
dc.languageen
dc.publisherElsevier BV
dc.subjectbrainwaves
dc.subjectelectroencephalogram
dc.subjectEEG
dc.subjectbrain-computer interface
dc.subjectBCI
dc.subjectbiometrics
dc.subjectauthentication
dc.subjectmachine learning
dc.subjectcoherence
dc.subjectfeature extraction
dc.titleBrainwave-based authentication using features fusion
dc.typejournal-article
dc.typeJournal Article
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:001053816800001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.volume129
plymouth.publication-statusPublished
plymouth.journalComputers & Security
dc.identifier.doi10.1016/j.cose.2023.103198
plymouth.organisational-group|Plymouth
plymouth.organisational-group|Plymouth|Faculty of Science and Engineering
plymouth.organisational-group|Plymouth|Faculty of Science and Engineering|School of Engineering, Computing and Mathematics
plymouth.organisational-group|Plymouth|REF 2021 Researchers by UoA
plymouth.organisational-group|Plymouth|Users by role
plymouth.organisational-group|Plymouth|Users by role|Academics
plymouth.organisational-group|Plymouth|REF 2021 Researchers by UoA|UoA11 Computer Science and Informatics
dcterms.dateAccepted2023-03-21
dc.date.updated2023-04-17T10:20:13Z
dc.rights.embargodate2023-4-18
dc.identifier.eissn1872-6208
dc.rights.embargoperiodforever
rioxxterms.versionofrecord10.1016/j.cose.2023.103198


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