{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T15:16:33Z","timestamp":1773414993645,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,17]],"date-time":"2021-03-17T00:00:00Z","timestamp":1615939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Foundation of China","award":["61572091"],"award-info":[{"award-number":["61572091"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, electroencephalogram (EEG) signals have been used as a biometric modality, and EEG-based biometric systems have received increasing attention. However, due to the sensitive nature of EEG signals, the extraction of identity information through processing techniques may lead to some loss in the extracted identity information. This may impact the distinctiveness between subjects in the system. In this context, we propose a new self-relative evaluation framework for EEG-based biometric systems. The proposed framework aims at selecting a more accurate identity information when the biometric system is open to the enrollment of novel subjects. The experiments were conducted on publicly available EEG datasets collected from 108 subjects in a resting state with closed eyes. The results show that the openness condition is useful for selecting more accurate identity information.<\/jats:p>","DOI":"10.3390\/s21062097","type":"journal-article","created":{"date-parts":[[2021,3,17]],"date-time":"2021-03-17T21:43:31Z","timestamp":1616017411000},"page":"2097","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Self-Relative Evaluation Framework for EEG-Based Biometric Systems"],"prefix":"10.3390","volume":"21","author":[{"given":"Meriem Romaissa","family":"Boubakeur","sequence":"first","affiliation":[{"name":"Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8521-5232","authenticated-orcid":false,"given":"Guoyin","family":"Wang","sequence":"additional","affiliation":[{"name":"Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101788","DOI":"10.1016\/j.cose.2020.101788","article-title":"A survey on methods and challenges in EEG based authentication","volume":"93","author":"Bidgoly","year":"2020","journal-title":"Comput. Secur."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Campisi, P., Scarano, G., Babiloni, F., Fallani, F.D., Colonnese, S., Maiorana, E., and Forastiere, L. (December, January 29). Brain waves based user recognition using the \u201ceyes closed resting conditions\u201d protocol. Proceedings of the 2011 IEEE International Workshop on Information Forensics and Security, Iguacu Falls, Brazil.","DOI":"10.1109\/WIFS.2011.6123138"},{"key":"ref_3","unstructured":"La Rocca, D., Campisi, P., and Scarano, G. (2012, January 6\u20137). EEG biometrics for individual recognition in resting state with closed eyes. Proceedings of the 2012 BIOSIG-Proceedings of the International Conference of Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany."},{"key":"ref_4","unstructured":"La Rocca, D., Campisi, P., and Scarano, G. (2013, January 11\u201314). On the Repeatability of EEG Features in a Biometric Recognition Framework using a Resting State Protocol. Proceedings of the BIOSIGNALS, Barcelona, Spain."},{"key":"ref_5","unstructured":"Z\u00faquete, A., Quintela, B., and da Silva Cunha, J.P. (2010, January 20\u201323). Biometric Authentication using Brain Responses to Visual Stimuli. Proceedings of the BIOSIGNALS, Valencia, Spain."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1109\/LSP.2016.2516043","article-title":"EEG biometrics using visual stimuli: A longitudinal study","volume":"23","author":"Das","year":"2016","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/s11265-007-0078-1","article-title":"EEG based biometric framework for automatic identity verification","volume":"49","author":"Palaniappan","year":"2007","journal-title":"J. Vlsi Signal Process. Syst. Signal Image Video Technol."},{"key":"ref_8","unstructured":"Sun, S. (2008, January 8\u201311). Multitask learning for EEG-based biometrics. Proceedings of the 2008 19th International Conference on Pattern Recognition, Tampa, FL, USA."},{"key":"ref_9","first-page":"85","article-title":"Method for improving EEG based emotion recognition by combining it with synchronized biometric and eye tracking technologies in a non-invasive and low cost way","volume":"10","author":"Gil","year":"2016","journal-title":"Front. Comput. Neurosci."},{"key":"ref_10","unstructured":"Tulceanu, V. (2012, January 27\u201331). Comprehensive brainwave authentication using emotional stimuli. Proceedings of the 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO), Bucharest, Romania."},{"key":"ref_11","first-page":"1","article-title":"Towards a minimal EEG channel array for a biometric system using resting-state and a genetic algorithm for channel selection","volume":"10","author":"Moctezuma","year":"2020","journal-title":"Sci. RepoRtS"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1109\/TIFS.2015.2481870","article-title":"On the permanence of EEG signals for biometric recognition","volume":"11","author":"Maiorana","year":"2015","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1007\/s10044-016-0569-4","article-title":"Task sensitivity in EEG biometric recognition","volume":"21","author":"Yang","year":"2018","journal-title":"Pattern Anal. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Dama\u0161evi\u010dius, R., Maskeli\u016bnas, R., Kazanavi\u010dius, E., and Wo\u017aniak, M. (2018). Combining cryptography with EEG biometrics. Comput. Intell. Neurosci., 2018.","DOI":"10.1155\/2018\/1867548"},{"key":"ref_16","unstructured":"Nguyen, P., Tran, D., Huang, X., and Sharma, D. (2012, January 16\u201319). A proposed feature extraction method for EEG-based person identification. Proceedings of the International Conference on Artificial Intelligence (ICAI). The Steering Committee of The World Congress in Computer Science, Computer, Las Vegas, NV, USA."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1109\/TIFS.2014.2308640","article-title":"Brain waves for automatic biometric-based user recognition","volume":"9","author":"Campisi","year":"2014","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_19","unstructured":"Mohammadi, G., Shoushtari, P., Molaee Ardekani, B., and Shamsollahi, M.B. (2006, January 24\u201326). Person identification by using AR model for EEG signals. In Proceeding of the World Academy of Science, Engineering and Technology, Prague, Czech Republic."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Han, C., Kim, S., Yoon, H., Lee, W., Park, C., Kim, K., and Park, K. (2015, January 7\u201312). Contrast between spectral and connectivity features for electroencephalography based authentication. Proceedings of the World Congress on Medical Physics and Biomedical Engineering, Toronto, ON, Canada.","DOI":"10.1007\/978-3-319-19387-8_298"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"2406","DOI":"10.1109\/TBME.2014.2317881","article-title":"Human brain distinctiveness based on EEG spectral coherence connectivity","volume":"61","author":"Campisi","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_23","unstructured":"Phung, D.Q., Tran, D., Ma, W., Nguyen, P., and Pham, T. (2014, January 23\u201325). Using Shannon Entropy as EEG Signal Feature for Fast Person Identification. Proceedings of the ESANN, Bruges, Belgium."},{"key":"ref_24","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_25","unstructured":"Poulos, M., Rangoussi, M., Chrissikopoulos, V., and Evangelou, A. (2016, January 5\u20138). Person identification based on parametric processing of the EEG. Proceedings of the ICECS\u201999, Proceedings of ICECS\u201999, 6th IEEE International Conference on Electronics, Circuits and Systems (Cat. No. 99EX357), Paphos, Cyprus."},{"key":"ref_26","first-page":"354","article-title":"High accuracy EEG biometrics identification using ICA and AR model","volume":"16","author":"Kaewwit","year":"2017","journal-title":"J. Inf. Commun. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Su, F., Xia, L., Cai, A., and Ma, J. (2010, January 10\u201313). Evaluation of recording factors in EEG-based personal identification: A vital step in real implementations. Proceedings of the 2010 IEEE International Conference on Systems, Man and Cybernetics, Istanbul, Turkey.","DOI":"10.1109\/ICSMC.2010.5641768"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1007\/s00138-016-0804-4","article-title":"EEG signal preprocessing for biometric recognition","volume":"27","author":"Maiorana","year":"2016","journal-title":"Mach. Vis. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hine, G.E., Maiorana, E., and Campisi, P. (2017, January 20\u201322). Resting-state EEG: A study on its non-stationarity for biometric applications. Proceedings of the 2017 International Conference of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany.","DOI":"10.23919\/BIOSIG.2017.8053519"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Boubakeur, M.R., Wang, G., Liu, K., and Benatchba, K. (2019, January 13\u201315). Evaluation of Identity Information Loss in EEG-Based Biometric Systems. Proceedings of the International Conference on Brain Informatics, Haikou, China.","DOI":"10.1007\/978-3-030-37078-7_20"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Fraschini, M., Meli, M., Demuru, M., Didaci, L., and Barberini, L. (2020). EEG Fingerprints under Naturalistic Viewing Using a Portable Device. Sensors, 20.","DOI":"10.3390\/s20226565"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/S0013-4694(97)00022-2","article-title":"Spatial filter selection for EEG-based communication","volume":"103","author":"McFarland","year":"1997","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_34","unstructured":"Masana, M., Liu, X., Twardowski, B., Menta, M., Bagdanov, A.D., and van de Weijer, J. (2020). Class-incremental learning: Survey and performance evaluation. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Th\u00f3risson, K.R., Bieger, J., Li, X., and Wang, P. (2019, January 6\u20139). Cumulative learning. Proceedings of the International Conference on Artificial General Intelligence, Shenzhen, China.","DOI":"10.1007\/978-3-030-27005-6_20"},{"key":"ref_36","unstructured":"Hoi, S.C., Sahoo, D., Lu, J., and Zhao, P. (2018). Online learning: A comprehensive survey. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Bendale, A., and Boult, T. (2015, January 7\u201312). Towards open world recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298799"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1109\/LSP.2014.2367091","article-title":"An EEG-based biometric system using eigenvector centrality in resting state brain networks","volume":"22","author":"Fraschini","year":"2014","journal-title":"IEEE Signal Process. Lett."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/6\/2097\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:36:53Z","timestamp":1760161013000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/6\/2097"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,17]]},"references-count":38,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["s21062097"],"URL":"https:\/\/doi.org\/10.3390\/s21062097","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,17]]}}}