{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T15:51:43Z","timestamp":1776441103158,"version":"3.51.2"},"reference-count":32,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,1,24]],"date-time":"2018-01-24T00:00:00Z","timestamp":1516752000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2017YFB1002502"],"award-info":[{"award-number":["2017YFB1002502"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61701089"],"award-info":[{"award-number":["61701089"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61601518"],"award-info":[{"award-number":["61601518"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61372172"],"award-info":[{"award-number":["61372172"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The electroencephalogram (EEG) signal represents a subject\u2019s specific brain activity patterns and is considered as an ideal biometric given its superior forgery prevention. However, the accuracy and stability of the current EEG-based person authentication systems are still unsatisfactory in practical application. In this paper, a multi-task EEG-based person authentication system combining eye blinking is proposed, which can achieve high precision and robustness. Firstly, we design a novel EEG-based biometric evoked paradigm using self- or non-self-face rapid serial visual presentation (RSVP). The designed paradigm could obtain a distinct and stable biometric trait from EEG with a lower time cost. Secondly, the event-related potential (ERP) features and morphological features are extracted from EEG signals and eye blinking signals, respectively. Thirdly, convolutional neural network and back propagation neural network are severally designed to gain the score estimation of EEG features and eye blinking features. Finally, a score fusion technology based on least square method is proposed to get the final estimation score. The performance of multi-task authentication system is improved significantly compared to the system using EEG only, with an increasing average accuracy from 92.4% to 97.6%. Moreover, open-set authentication tests for additional imposters and permanence tests for users are conducted to simulate the practical scenarios, which have never been employed in previous EEG-based person authentication systems. A mean false accepted rate (FAR) of 3.90% and a mean false rejected rate (FRR) of 3.87% are accomplished in open-set authentication tests and permanence tests, respectively, which illustrate the open-set authentication and permanence capability of our systems.<\/jats:p>","DOI":"10.3390\/s18020335","type":"journal-article","created":{"date-parts":[[2018,1,24]],"date-time":"2018-01-24T09:47:36Z","timestamp":1516787256000},"page":"335","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":64,"title":["An EEG-Based Person Authentication System with Open-Set Capability Combining Eye Blinking Signals"],"prefix":"10.3390","volume":"18","author":[{"given":"Qunjian","family":"Wu","sequence":"first","affiliation":[{"name":"China National Digital Switching System Engineering and Technological Research Center, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Zeng","sequence":"additional","affiliation":[{"name":"China National Digital Switching System Engineering and Technological Research Center, Zhengzhou 450001, China"},{"name":"Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chi","family":"Zhang","sequence":"additional","affiliation":[{"name":"China National Digital Switching System Engineering and Technological Research Center, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Tong","sequence":"additional","affiliation":[{"name":"China National Digital Switching System Engineering and Technological Research Center, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Yan","sequence":"additional","affiliation":[{"name":"China National Digital Switching System Engineering and Technological Research Center, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1080\/2326263X.2015.1134958","article-title":"Recent advances and open challenges in hybrid brain-computer interfacing: A technological review of non-invasive human research","volume":"3","author":"Banville","year":"2016","journal-title":"Brain-Comput. Interfaces"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1163","DOI":"10.1109\/TIP.2016.2515987","article-title":"Robust point set matching for partial face recognition","volume":"25","author":"Weng","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_3","first-page":"39","article-title":"Fingerprint recognition with artificial neural networks: Application to e-learning","volume":"8","author":"Kouamo","year":"2016","journal-title":"J. Intell. Learn. Syst. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.intcom.2010.10.001","article-title":"Usability evaluation of voiceprint authentication in automated telephone banking: Sentences versus digits","volume":"23","author":"Gunson","year":"2011","journal-title":"Interact. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.patrec.2016.02.001","article-title":"Iris recognition through machine learning techniques: A survey","volume":"82","author":"Marsico","year":"2016","journal-title":"Pattern Recognit. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1049\/iet-bmt.2014.0040","article-title":"State-of-the-art methods and future perspectives for personal recognition based on electroencephalogram signals","volume":"4","author":"Ahmed","year":"2015","journal-title":"IET Biom."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1109\/TPAMI.2007.1013","article-title":"Biometrics from brain electrical activity: A machine learning approach","volume":"29","author":"Palaniappan","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.tics.2011.03.006","article-title":"Understanding complexity in the human brain","volume":"15","author":"Bassett","year":"2011","journal-title":"Trends Cognit. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6537","DOI":"10.1016\/j.eswa.2014.05.013","article-title":"Electroencephalogram subject identification: A review","volume":"41","author":"Alonso","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_10","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, Pafos, Cyprus."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ravi, K.V.R., and Palaniappan, R. (2005, January 21\u201324). Leave-one-out authentication of persons using 40 hz eeg oscillations. Proceedings of the International Conference on Computer as a Tool, Belgrade, Serbia.","DOI":"10.1109\/EURCON.2005.1630219"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1037\/0278-7393.6.2.174","article-title":"A standardized set of 260 pictures: Norms for name agreement, image agreement, familiarity, and visual complexity","volume":"6","author":"Snodgrass","year":"1980","journal-title":"J. Exp. Psychol. Hum. Learn. Mem."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Shiliang, S. (2008, January 8\u201311). Multitask learning for eeg-based biometrics. Proceedings of the 2008 19th International Conference on Pattern Recognition, Tampa, FL, USA.","DOI":"10.1109\/ICPR.2008.4761865"},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"1666","DOI":"10.1016\/j.neuroimage.2010.01.030","article-title":"Eeg evidence of face-specific visual self-representation","volume":"50","author":"Miyakoshi","year":"2010","journal-title":"Neuroimage"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1016\/j.neuroimage.2008.03.054","article-title":"Face-specific and domain-general characteristics of cortical responses during self-recognition","volume":"42","author":"Sugiura","year":"2008","journal-title":"Neuroimage"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2143","DOI":"10.1016\/j.ijleo.2015.09.020","article-title":"Individual identification based on neuro-signal using motor movement and imaginary cognitive process","volume":"127","author":"Sharma","year":"2016","journal-title":"Opt. Int. J. Light Electron Opt."},{"key":"ref_18","first-page":"91","article-title":"Rapid serial visual presentation (rsvp): A method for studying language processing","volume":"118","author":"Potter","year":"1984","journal-title":"New Methods Read. Compr. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1016\/j.clinph.2012.12.050","article-title":"Gaze-independent bci-spelling using rapid serial visual presentation (rsvp)","volume":"124","author":"Acqualagna","year":"2013","journal-title":"Clin. Neurophysiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/TNSRE.2005.862695","article-title":"Robust classification of eeg signal for brain-computer interface","volume":"14","author":"Thulasidas","year":"2006","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wu, Q., Zeng, Y., Lin, Z., Wang, X., and Yan, B. (2017, January 25\u201328). Real-time eeg-based person authentication system using face rapid serial visual presentation. Proceedings of the 8th International IEEE EMBS Conference On Neural Engineering, Shanghai, China.","DOI":"10.1109\/NER.2017.8008414"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Pham, T., Ma, W., Tran, D., and Nguyen, P. (2014, January 6\u201311). Multi-factor eeg-based user authentication. Proceedings of the International Joint Conference on Neural Networks, Beijing, China.","DOI":"10.1109\/IJCNN.2014.6889569"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"13422","DOI":"10.1109\/ACCESS.2017.2718003","article-title":"Biometrics based on hand synergies and their neural representations","volume":"5","author":"Patel","year":"2017","journal-title":"IEEE Access"},{"key":"ref_24","first-page":"876","article-title":"A novel biometric approach for human identification and verification using eye blinking signal","volume":"22","author":"Ahmed","year":"2014","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1088\/0967-3334\/27\/4\/008","article-title":"Automatic removal of the eye blink artifact from eeg using an ica-based template matching approach","volume":"27","author":"Li","year":"2006","journal-title":"Physiol. Meas."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2635","DOI":"10.1109\/TIFS.2016.2577551","article-title":"A high-security eeg-based login system with rsvp stimuli and dry electrodes","volume":"11","author":"Chen","year":"2016","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/72.554195","article-title":"Face recognition: A convolutional neural-network approach","volume":"8","author":"Giles","year":"1997","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Li, J., Zhang, Z., and He, H. (2016, January 28\u201330). Implementation of eeg emotion recognition system based on hierarchical convolutional neural networks. Proceedings of the International Conference on Brain Inspired Cognitive Systems, Beijing, China.","DOI":"10.1007\/978-3-319-49685-6_3"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.neucom.2015.04.025","article-title":"Brainprint: Assessing the uniqueness, collectability, and permanence of a novel method for erp biometrics","volume":"166","author":"Armstrong","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1109\/TPAMI.2007.1012","article-title":"Person authentication using brainwaves (eeg) and maximum a posteriori model adaptation","volume":"29","author":"Marcel","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","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"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/2\/335\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:52:26Z","timestamp":1760194346000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/2\/335"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,24]]},"references-count":32,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2018,2]]}},"alternative-id":["s18020335"],"URL":"https:\/\/doi.org\/10.3390\/s18020335","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1,24]]}}}