{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:53:09Z","timestamp":1760230389193,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T00:00:00Z","timestamp":1658707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Regional Operational Program of the Masovian Voivodeship","award":["01.02.00-14-b459\/18"],"award-info":[{"award-number":["01.02.00-14-b459\/18"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The paper is devoted to the study of EEG-based people verification. Analyzed solutions employed shallow artificial neural networks using spectral EEG features as input representation. We investigated the impact of the features derived from different frequency bands and their combination on verification results. Moreover, we studied the influence of a number of hidden neurons in a neural network. The datasets used in the analysis consisted of signals recorded during resting state from 29 healthy adult participants performed on different days, 20 EEG sessions for each of the participants. We presented two different scenarios of training and testing processes. In the first scenario, we used different parts of each recording session to create the training and testing datasets, and in the second one, training and testing datasets originated from different recording sessions. Among single frequency bands, the best outcomes were obtained for the beta frequency band (mean accuracy of 91 and 89% for the first and second scenarios, respectively). Adding the spectral features from more frequency bands to the beta band features improved results (95.7 and 93.1%). The findings showed that there is not enough evidence that the results are different between networks using different numbers of hidden neurons. Additionally, we included results for the attack of 23 external impostors whose recordings were not used earlier in training or testing the neural network in both scenarios. Another significant finding of our study shows worse sensitivity results in the second scenario. This outcome indicates that most of the studies presenting verification or identification results based on the first scenario (dominating in the current literature) are overestimated when it comes to practical applications.<\/jats:p>","DOI":"10.3390\/s22155529","type":"journal-article","created":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T04:52:47Z","timestamp":1658724767000},"page":"5529","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Impact of EEG Frequency Bands and Data Separation on the Performance of Person Verification Employing Neural Networks"],"prefix":"10.3390","volume":"22","author":[{"given":"Renata","family":"Pluci\u0144ska","sequence":"first","affiliation":[{"name":"Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4303-9450","authenticated-orcid":false,"given":"Konrad","family":"J\u0119drzejewski","sequence":"additional","affiliation":[{"name":"Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marek","family":"Walig\u00f3ra","sequence":"additional","affiliation":[{"name":"Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology, 02-093 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Urszula","family":"Malinowska","sequence":"additional","affiliation":[{"name":"Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology, 02-093 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5298-920X","authenticated-orcid":false,"given":"Jacek","family":"Rogala","sequence":"additional","affiliation":[{"name":"Institute of Physiology and Pathology of Hearing, Bioimaging Research Center, World Hearing Center, Kajetany, 05-830 Nadarzyn, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,25]]},"reference":[{"key":"ref_1","unstructured":"Zheng, X. (2019). EEG-Based Brain-Computer Interfaces. Neural Interface: Frontiers and Applications. Advances in Experimental Medicine and Biology, vol 1101, Springer."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e13874","DOI":"10.1111\/psyp.13874","article-title":"EEG-neurofeedback and executive function enhancement in healthy adults: A systematic review","volume":"58","author":"Viviani","year":"2021","journal-title":"Psychophysiology"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12938-017-0391-2","article-title":"Physiological artifacts in scalp EEG and ear-EEG","volume":"16","author":"Kappel","year":"2017","journal-title":"Biomed. Eng. Online"},{"key":"ref_4","first-page":"1","article-title":"Comparison of Smoothing Filters in Analysis of EEG Data for the Medical Diagnostics Purposes","volume":"20","author":"Podpora","year":"2020","journal-title":"Sensors"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1016\/S0013-4694(97)00147-8","article-title":"Volume conduction effects in EEG and MEG","volume":"106","author":"Reinders","year":"1998","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/s21227710","article-title":"Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features","volume":"21","author":"Malekzadeh","year":"2021","journal-title":"Sensors"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1038\/s41398-020-0781-2","article-title":"Machine learning classification of ADHD and HC by multimodal serotonergic data","volume":"10","author":"Kautzky","year":"2020","journal-title":"Transl. Psychiatry"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Phan, T.-D.T., Kim, S.-H., Yang, H.-J., and Lee, G.-S. (2021). EEG-Based Emotion Recognition by Convolutional Neural Network with Multi-Scale Kernels. Sensors, 21.","DOI":"10.3390\/s21155092"},{"key":"ref_9","unstructured":"(2022). ISO\/IEC 2382-37:2022 Information Technology\u2014Vocabulary\u2014Part 37: Biometrics, ISO\/IEC. [3rd ed.]."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Jijomon, C.M., and Vinod, A.P. (2018, January 7\u20139). EEG-based Biometric Identification using Frequently Occurring Maximum Power Spectral Features. Proceedings of the 2018 IEEE Applied Signal Processing Conference, Kolkata, India.","DOI":"10.1109\/ASPCON.2018.8748581"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"5229576","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_13","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":"Arezoumand","year":"2020","journal-title":"Comput. Secur."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"056019","DOI":"10.1088\/1741-2560\/12\/5\/056019","article-title":"EEG biometric identification: A thorough exploration of the time-frequency domain","volume":"12","author":"Travieso","year":"2015","journal-title":"J. Neural Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"958","DOI":"10.1109\/THMS.2017.2682115","article-title":"On the Usability of Electroencephalographic Signals for Biometric Recognition: A Survey","volume":"47","author":"Yang","year":"2017","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"ref_16","unstructured":"Arias-Cabarcos, P., Habrich, T., Becker, K., Becker, C., and Strufe, T. (2021, January 11\u201313). Inexpensive Brainwave Authentication: New Techniques and Insights on User Acceptance. Proceedings of the 30th USENIX Security Symposium (USENIX Security 21), Virtual."},{"key":"ref_17","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_18","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_19","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1002\/hbm.20468","article-title":"Heritability of \u201cSmall-World\u201d Networks in the Brain: A Graph Theoretical Analysis of Resting-State EEG Functional Connectivity","volume":"29","author":"Smit","year":"2008","journal-title":"Hum. Brain Mapp."},{"key":"ref_20","first-page":"562","article-title":"Heritability of human brain functioning as assessed by electroencephalosraphy","volume":"58","author":"Molenaar","year":"1996","journal-title":"Am. J. Hum. Genet."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1002\/mrm.1910340409","article-title":"Functional connectivity in the motor cortex of resting human brain using echo-planar MRI","volume":"34","author":"Biswal","year":"1995","journal-title":"Magn. Reson. Med."},{"key":"ref_22","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, ICASSP99 (Cat. No.99CH36258), Phoenix, AZ, USA.","DOI":"10.1109\/ICASSP.1999.759940"},{"key":"ref_23","first-page":"1133","article-title":"Analysis of the EEG Signal for a Practical Biometric System","volume":"44","author":"Abdullah","year":"2010","journal-title":"World Acad. Sci. Eng. Technol."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","unstructured":"Ma, L., Minett, J.W., Blu, T., and Wang, W.S.-Y. (2015, January 25\u201329). Resting State EEG-based biometrics for individual identification using convolutional neural networks. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Milan, Italy.","DOI":"10.1109\/EMBC.2015.7318985"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Nakanishi, I., Baba, S., and Miyamoto, C. (2009, January 7\u20139). EEG based biometric authentication using new spectral features. Proceedings of the 2009 International Symposium on Intelligent Signal Processing and Communication Systems, Kanazawa, Japan.","DOI":"10.1109\/ISPACS.2009.5383756"},{"key":"ref_27","unstructured":"Cheng, C.-Y. (2013). EEG-Based Person Identification System and Its Longitudinal Adaptation. [Master\u2019s Thesis, National Chiao Tung University]."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.patrec.2021.01.004","article-title":"Learning deep features for task-independent EEG-based biometric verification","volume":"143","author":"Maiorana","year":"2021","journal-title":"Pattern Recognit. Lett."},{"key":"ref_29","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":"2016","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1109\/TIFS.2017.2778010","article-title":"Longitudinal Evaluation of EEG-Based Biometric Recognition","volume":"13","author":"Maiorana","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_31","first-page":"419","article-title":"Personal Identification by EEG Using ICA and Neural Network","volume":"Volume 6018 LNCS","author":"Taniar","year":"2010","journal-title":"Proceedings of the Computational Science and Its Applications\u2014ICCSA 2010"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"66","DOI":"10.3389\/fninf.2018.00066","article-title":"Challenges and Future Perspectives on Electroencephalogram-Based Biometrics in Person Recognition","volume":"12","author":"Chan","year":"2018","journal-title":"Front. Neuroinform."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1109\/TIFS.2014.2308640","article-title":"La Brain waves for automatic biometric-based user recognition","volume":"9","author":"Campisi","year":"2014","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2382577.2382579","article-title":"Leakage in data mining: Formulation, detection, and avoidance","volume":"6","author":"Kaufman","year":"2012","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/0013-4694(58)90053-1","article-title":"Report of the committee on methods of clinical examination in electroencephalography: 1957","volume":"10","author":"Jasper","year":"1958","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1049\/iet-bmt.2019.0158","article-title":"EEG-based biometric identification using frequency-weighted power feature","volume":"9","author":"Jijomon","year":"2020","journal-title":"IET Biom."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0167-7012(00)00201-3","article-title":"Artificial neural networks: Fundamentals, computing, design, and application","volume":"43","author":"Basheer","year":"2000","journal-title":"J. Microbiol. Methods"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1109\/72.329697","article-title":"Training feedforward networks with the Marquardt algorithm","volume":"5","author":"Hagan","year":"1994","journal-title":"IEEE Trans. Neural. Netw."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"247","DOI":"10.55782\/ane-2000-1344","article-title":"Beta activity: A carrier for visual attention","volume":"60","year":"2000","journal-title":"Acta Neurobiol. Exp."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.ijpsycho.2011.11.006","article-title":"Beta band oscillations engagement in human alertness process","volume":"85","author":"Brzezicka","year":"2012","journal-title":"Int. J. Psychophysiol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1097\/00006842-197701000-00006","article-title":"Alpha Feedback\u2014A Comparison of Two Control Groups","volume":"39","author":"Williams","year":"1977","journal-title":"Psychosom. Med."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fnhum.2017.00119","article-title":"Beware: Recruitment of Muscle Activity by the EEG-Neurofeedback Trainings of High Frequencies","volume":"11","author":"Paluch","year":"2017","journal-title":"Front. Hum. Neurosci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1111\/j.1469-8986.1977.tb01171.x","article-title":"Multiple Response Comparison of Parietal EEG and Frontalis EMG Biofeedback","volume":"14","author":"DeGood","year":"1977","journal-title":"Psychophysiology"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s10484-013-9213-x","article-title":"Differential EMG Biofeedback for Children with ADHD: A Control Method for Neurofeedback Training with a Case Illustration","volume":"38","author":"Maurizio","year":"2013","journal-title":"Appl. Psychophysiol. Biofeedback"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1137\/0150009","article-title":"Oscillator Death in Systems of Coupled Neural Oscillators","volume":"50","author":"Ermentrout","year":"1990","journal-title":"SIAM J. Appl. Math."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1867","DOI":"10.1073\/pnas.97.4.1867","article-title":"Gamma rhythms and beta rhythms have different synchronization properties","volume":"97","author":"Kopell","year":"2000","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1007\/s10827-010-0268-x","article-title":"Stability of two cluster solutions in pulse coupled networks of neural oscillators","volume":"30","author":"Chandrasekaran","year":"2011","journal-title":"J. Comput. Neurosci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"10655","DOI":"10.1007\/s11042-019-7258-4","article-title":"EEG-based biometric identification with convolutional neural network","volume":"79","author":"Chen","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1109\/LSP.2019.2906826","article-title":"Adversarial Deep Learning in EEG Biometrics","volume":"26","author":"Wang","year":"2019","journal-title":"IEEE Signal Process. Lett."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/15\/5529\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:55:53Z","timestamp":1760140553000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/15\/5529"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,25]]},"references-count":49,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["s22155529"],"URL":"https:\/\/doi.org\/10.3390\/s22155529","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,7,25]]}}}