{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T16:39:20Z","timestamp":1772642360647,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T00:00:00Z","timestamp":1663027200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Conventional biometrics have been employed in high-security user-authentication systems for over 20 years now. However, some of these modalities face low-security issues in common practice. Brainwave-based user authentication has emerged as a promising alternative method, as it overcomes some of these drawbacks and allows for continuous user authentication. In the present study, we address the problem of individual user variability, by proposing a data-driven Electroencephalography (EEG)-based authentication method. We introduce machine learning techniques, in order to reveal the optimal classification algorithm that best fits the data of each individual user, in a fast and efficient manner. A set of 15 power spectral features (delta, theta, lower alpha, higher alpha, and alpha) is extracted from three EEG channels. The results show that our approach can reliably grant or deny access to the user (mean accuracy of 95.6%), while at the same time poses a viable option for real-time applications, as the total time of the training procedure was kept under one minute.<\/jats:p>","DOI":"10.3390\/s22186929","type":"journal-article","created":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T22:37:28Z","timestamp":1663108648000},"page":"6929","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["A Personalized User Authentication System Based on EEG Signals"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4675-6156","authenticated-orcid":false,"given":"Christos","family":"Stergiadis","sequence":"first","affiliation":[{"name":"Department of Psychology, City College, University of York Europe Campus, 54622 Thessaloniki, Greece"},{"name":"Neuroscience Research Center (NEUREC), City College, University of York Europe Campus, 54622 Thessaloniki, Greece"}]},{"given":"Vasiliki-Despoina","family":"Kostaridou","sequence":"additional","affiliation":[{"name":"Department of Psychology, City College, University of York Europe Campus, 54622 Thessaloniki, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8197-6667","authenticated-orcid":false,"given":"Simos","family":"Veloudis","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City College, University of York Europe Campus, 54622 Thessaloniki, Greece"}]},{"given":"Dimitrios","family":"Kazis","sequence":"additional","affiliation":[{"name":"3rd Department of Neurology, Aristotle University of Thessaloniki, Exochi, 57010 Thessaloniki, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1629-6446","authenticated-orcid":false,"given":"Manousos A.","family":"Klados","sequence":"additional","affiliation":[{"name":"Department of Psychology, City College, University of York Europe Campus, 54622 Thessaloniki, Greece"},{"name":"Neuroscience Research Center (NEUREC), City College, University of York Europe Campus, 54622 Thessaloniki, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ashby, C., Bhatia, A., Tenore, F., and Vogelstein, J. (May, January 27). Low-Cost Electroencephalogram (EEG) Based Authentication. Proceedings of the 2011 5th International IEEE\/EMBS Conference on Neural Engineering, Cancun, Mexico.","DOI":"10.1109\/NER.2011.5910581"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Adams, A.A., Brenner, M., and Smith, M. (2013). I Think, Therefore I Am: Usability and Security of Authentication Using Brainwaves. Proceedings of the Financial Cryptography and Data Security, Springer.","DOI":"10.1007\/978-3-642-41320-9"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Gui, Q., Jin, Z., and Xu, W. (2014, January 13). Exploring EEG-Based Biometrics for User Identification and Authentication. Proceedings of the 2014 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), Philadelphia, PA, USA.","DOI":"10.1109\/SPMB.2014.7002950"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Jayarathne, I., Cohen, M., and Amarakeerthi, S. (2016, January 13\u201315). BrainID: Development of an EEG-Based Biometric Authentication System. Proceedings of the 2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada.","DOI":"10.1109\/IEMCON.2016.7746325"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.patrec.2015.07.034","article-title":"A New Multi-Level Approach to EEG Based Human Authentication Using Eye Blinking","volume":"82","author":"Ahmed","year":"2016","journal-title":"Pattern Recognit. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Nguyen, B., Nguyen, D., Ma, W., and Tran, D. (2017, January 14\u201319). Investigating the Possibility of Applying EEG Lossy Compression to EEG-Based User Authentication. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7965839"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"de la Puerta, J.G., Ferreira, I.G., Bringas, P.G., Klett, F., Abraham, A., de Carvalho, A.C.P.L.F., Herrero, \u00c1., Baruque, B., Quinti\u00e1n, H., and Corchado, E. (2014, January 25\u201327). EEG-Based User Authentication Using Artifacts. Proceedings of the International Joint Conference SOCO\u201914-CISIS\u201914-ICEUTE\u201914, Bilbao, Spain.","DOI":"10.1007\/978-3-319-07995-0"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Phothisonothai, M. (2015, January 16\u201319). An Investigation of Using SSVEP for EEG-Based User Authentication System. Proceedings of the 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), Hong Kong, China.","DOI":"10.1109\/APSIPA.2015.7415406"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wong, R.-Z., Choo, Y.-H., and Muda, A.K. (2020). Task Sensitivity in Continuous Electroencephalogram Person Authentication. Int. J. Adv. Comput. Sci. Appl. IJACSA, 11.","DOI":"10.14569\/IJACSA.2020.0110270"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wu, Q., Zeng, Y., Zhang, C., Tong, L., and Yan, B. (2018). An EEG-Based Person Authentication System with Open-Set Capability Combining Eye Blinking Signals. Sensors, 18.","DOI":"10.3390\/s18020335"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1016\/j.patcog.2012.10.023","article-title":"Person Authentication from Neural Activity of Face-Specific Visual Self-Representation","volume":"46","author":"Yeom","year":"2013","journal-title":"Pattern Recognit."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yu, T., Wei, C.-S., Chiang, K.-J., Nakanishi, M., and Jung, T.-P. (2019, January 20\u201323). EEG-Based User Authentication Using a Convolutional Neural Network. Proceedings of the 2019 9th International IEEE\/EMBS Conference on Neural Engineering (NER), San Francisco, CA, USA.","DOI":"10.1109\/NER.2019.8716965"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., and Wang, W. (2013, January 14\u201316). EEG-Based User Authentication in Multilevel Security Systems. Proceedings of the Advanced Data Mining and Applications, Hangzhou, China.","DOI":"10.1007\/978-3-642-53914-5"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Valsaraj, A., Madala, I., Garg, N., Patil, M., and Baths, V. (October, January 29). Motor Imagery Based Multimodal Biometric User Authentication System Using EEG. Proceedings of the 2020 International Conference on Cyberworlds (CW), Caen, France.","DOI":"10.1109\/CW49994.2020.00050"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Szu, H., Hsu, C., Szu, C., and Wang, S. (2003, January 22\u201325). Live Biometric Authenticity Check. Proceedings of the Independent Component Analyses, Wavelets, and Neural Networks, Orlando, FL, USA.","DOI":"10.1117\/12.502477"},{"key":"ref_16","unstructured":"He, Z., Wang, W., Dong, J., and Tan, T. (2021). Temporal Sparse Adversarial Attack on Sequence-Based Gait Recognition. arXiv."},{"key":"ref_17","unstructured":"Stallings, W., and Brown, L. (2018). Computer Security: Principles and Practice, Pearson. [4th ed.]."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, M., Abbass, H.A., and Hu, J. (2016, January 12\u201314). Continuous authentication using EEG and face images for trusted autonomous systems. Proceedings of the 2016 14th Annual Conference on Privacy, Security and Trust (PST), Auckland, New Zealand.","DOI":"10.1109\/PST.2016.7906958"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Pham, T., Ma, W., Tran, D., Nguyen, P., and Phung, D. (2014, January 6\u201311). Multi-Factor EEG-Based User Authentication. Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, China.","DOI":"10.1109\/IJCNN.2014.6889569"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Poulos, M., Rangoussi, M., and Alexandris, N. (1999, January 15\u201319). Neural Network Based Person Identification Using EEG Features. Proceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing, Phoenix, AZ, USA.","DOI":"10.1109\/ICASSP.1999.759940"},{"key":"ref_21","unstructured":"Paranjape, R.B., Mahovsky, J., Benedicenti, L., and Koles\u2019, Z. (2001, January 13\u201316). The Electroencephalogram as a Biometric. Proceedings of the Canadian Conference on Electrical and Computer Engineering 2001, Toronto, ON, Canada."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s00034-017-0551-4","article-title":"EEG-Based Biometric Authentication Using Gamma Band Power During Rest State","volume":"37","author":"Thomas","year":"2018","journal-title":"Circuits Syst. Signal. Process."},{"key":"ref_23","unstructured":"Poulos, M., Rangoussi, M., Chrissikopoulos, V., and Evangelou, A. (1999, January 5\u20138). Person Identification Based on Parametric Processing of the EEG. Proceedings of the 6th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.99EX357), Pafos, Cyprus."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1055\/s-0038-1634316","article-title":"Person Identification from the EEG Using Nonlinear Signal Classification","volume":"41","author":"Poulos","year":"2002","journal-title":"Methods Inf. Med."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hema, C.R., and Osman, A.A. (2010, January 21\u201323). Single Trial Analysis on EEG Signatures to Identify Individuals. Proceedings of the 2010 6th International Colloquium on Signal Processing & its Applications, Malacca City, Malaysia.","DOI":"10.1109\/CSPA.2010.5545313"},{"key":"ref_26","unstructured":"Mu, Z., and Hu, J. (2009, January 13\u201314). Research of EEG Identification Computing Based on AR Model. Proceedings of the 2009 International Conference on Future BioMedical Information Engineering (FBIE), Sanya, China."},{"key":"ref_27","first-page":"e1867548","article-title":"Combining Cryptography with EEG Biometrics","volume":"2018","year":"2018","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1109\/LSP.2019.2906826","article-title":"Adversarial Deep Learning in EEG Biometrics","volume":"26","author":"Ozdenizci","year":"2019","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"10500","DOI":"10.1109\/ACCESS.2021.3135805","article-title":"EEG Channel Selection for Person Identification Using Binary Grey Wolf Optimizer","volume":"10","author":"Alyasseri","year":"2022","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Thornton, C., Hutter, F., Hoos, H.H., and Leyton-Brown, K. (2013, January 11\u201314). Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA.","DOI":"10.1145\/2487575.2487629"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e549419","DOI":"10.1155\/2009\/549419","article-title":"A Framework Combining Delta Event-Related Oscillations (EROs) and Synchronisation Effects (ERD\/ERS) to Study Emotional Processing","volume":"2009","author":"Klados","year":"2009","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Palmer, J.A., Makeig, S., Kreutz-Delgado, K., and Rao, B.D. (April, January 31). Newton Method for the ICA Mixture Model. Proceedings of the 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, Las Vegas, NV, USA.","DOI":"10.1109\/ICASSP.2008.4517982"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Klados, M.A., Papadelis, C.L., and Bamidis, P.D. (2009, January 4\u20137). REG-ICA: A New Hybrid Method for EOG Artifact Rejection. Proceedings of the 2009 9th International Conference on Information Technology and Applications in Biomedicine, Larnaka, Cyprus.","DOI":"10.1109\/ITAB.2009.5394295"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.bspc.2011.02.001","article-title":"REG-ICA: A Hybrid Methodology Combining Blind Source Separation and Regression Techniques for the Rejection of Ocular Artifacts","volume":"6","author":"Klados","year":"2011","journal-title":"Biomed. Signal. Process. Control"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.jneumeth.2003.10.009","article-title":"EEGLAB: An Open Source Toolbox for Analysis of Single-Trial EEG Dynamics Including Independent Component Analysis","volume":"134","author":"Delorme","year":"2004","journal-title":"J. Neurosci. Methods"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.neuroimage.2019.05.026","article-title":"ICLabel: An Automated Electroencephalographic Independent Component Classifier, Dataset, and Website","volume":"198","author":"Makeig","year":"2019","journal-title":"NeuroImage"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1145\/1656274.1656278","article-title":"The WEKA data mining software: An update","volume":"11","author":"Hall","year":"2009","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Mu, Z., Hu, J., and Min, J. (2016). EEG-Based Person Authentication Using a Fuzzy Entropy-Related Approach with Two Electrodes. Entropy, 18.","DOI":"10.3390\/e18120432"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Liew, S.-H., Choo, Y.-H., Low, Y.F., Yusoh, Z.I.M., Yap, T.-B., and Muda, A.K. (2015). Comparing Features Extraction Methods for Person Authentication Using EEG Signals. Pattern Analysis, Intelligent Security and the Internet of Things, Springer.","DOI":"10.1007\/978-3-319-17398-6_21"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e5229576","DOI":"10.1155\/2021\/5229576","article-title":"Review on EEG-Based Authentication Technology","volume":"2021","author":"Zhang","year":"2021","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Mao, Z., Yao, W.X., and Huang, Y. (2017, January 25\u201328). EEG-based biometric identification with deep learning. Proceedings of the 2017 8th International IEEE\/EMBS Conference on Neural Engineering (NER), Shanghai, China.","DOI":"10.1109\/NER.2017.8008425"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Puengdang, S., Tuarob, S., Sattabongkot, T., and Sakboonyarat, B. (2019, January 23\u201326). EEG-Based Person Authentication Method Using Deep Learning with Visual Stimulation. Proceedings of the 2019 11th International Conference on Knowledge and Smart Technology (KST), Phuket, Thailand.","DOI":"10.1109\/KST.2019.8687819"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/18\/6929\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:30:43Z","timestamp":1760142643000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/18\/6929"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,13]]},"references-count":42,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["s22186929"],"URL":"https:\/\/doi.org\/10.3390\/s22186929","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,13]]}}}