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EEG Authentication System Based on One- and Multi-Class Machine Learning Classifiers

Sensors (Basel, Switzerland), 2022-12, Vol.23 (1), p.186 [Peer Reviewed Journal]

COPYRIGHT 2022 MDPI AG ;2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2022 by the authors. 2022 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s23010186 ;PMID: 36616785

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Title:
EEG Authentication System Based on One- and Multi-Class Machine Learning Classifiers
Author: Hernández-Álvarez, Luis ; Barbierato, Elena ; Caputo, Stefano ; Mucchi, Lorenzo ; Hernández Encinas, Luis
Subjects: Algorithms ; Artificial intelligence ; Authentication ; Biometrics ; Chi-square test ; Classifiers ; Data encryption chips ; Electrocardiography ; electroencephalogram ; Electroencephalography ; Hybrid systems ; Machine learning ; multi-class classifier ; one-class classifiers ; Online payment services ; Outliers (statistics) ; Physiology ; Principal components analysis ; Privacy ; Smartphones ; Software ; Statistical tests ; Support vector machines ; Usability ; user authentication
Is Part Of: Sensors (Basel, Switzerland), 2022-12, Vol.23 (1), p.186
Description: In the current Information Age, it is usual to access our personal and professional information, such as bank account data or private documents, in a telematic manner. To ensure the privacy of this information, user authentication systems should be accurately developed. In this work, we focus on biometric authentication, as it depends on the user's inherent characteristics and, therefore, offers personalized authentication systems. Specifically, we propose an electrocardiogram (EEG)-based user authentication system by employing One-Class and Multi-Class Machine Learning classifiers. In this sense, the main novelty of this article is the introduction of Isolation Forest and Local Outlier Factor classifiers as new tools for user authentication and the investigation of their suitability with EEG data. Additionally, we identify the EEG channels and brainwaves with greater contribution to the authentication and compare them with the traditional dimensionality reduction techniques, Principal Component Analysis, and χ2 statistical test. In our final proposal, we elaborate on a hybrid system resistant to random forgery attacks using an Isolation Forest and a Random Forest classifiers, obtaining a final accuracy of 82.3%, a precision of 91.1% and a recall of 75.3%.
Publisher: Switzerland: MDPI AG
Language: English
Identifier: ISSN: 1424-8220
EISSN: 1424-8220
DOI: 10.3390/s23010186
PMID: 36616785
Source: Open Access: PubMed Central
Geneva Foundation Free Medical Journals at publisher websites
AUTh Library subscriptions: ProQuest Central
ROAD: Directory of Open Access Scholarly Resources
DOAJ Directory of Open Access Journals

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EEG Authentication System Based on One- and Multi-Class Machine Learning Classifiers

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