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%.
This work was supported in part by Comunidad de Madrid (Spain) through Project CYNAMON, Grant No. P2018/TCS-4566-CM, co-funded by the European Regional Development Fund (ESF, FEDER and ERDF, EU); in part by the European Union’s Horizon 2020 Research and Innovation Program under Grant No. 872752 and under Grant No. 101017141; in part by the European Telecommunication Standard Institute (ETSI) technical committee (TC) on Smart Body Area Network (SmartBAN). L.H.-Á. would like to thank CSIC Project CASDiM for its support. E.B would like to thank UNICESV (Centro Universitario Di Ricerca Per Lo Sviluppo Competitivo Del Settore Vitivinicolo). We want to thank the reviewer for their useful comments and recommendations.