{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/applying-advanced-machine-learning-models-to","title":"Applying advanced machine learning models to classify electro-physiological activity of human brain for use in biometric identification","arxiv_id":"1708.01167","date":"2017-08-03","proceeding":null,"authors":["Iaroslav Omelianenko"],"abstract":"In this article we present the results of our research related to the study\nof correlations between specific visual stimulation and the elicited brain's\nelectro-physiological response collected by EEG sensors from a group of\nparticipants. We will look at how the various characteristics of visual\nstimulation affect the measured electro-physiological response of the brain and\ndescribe the optimal parameters found that elicit a steady-state visually\nevoked potential (SSVEP) in certain parts of the cerebral cortex where it can\nbe reliably perceived by the electrode of the EEG device. After that, we\ncontinue with a description of the advanced machine learning pipeline model\nthat can perform confident classification of the collected EEG data in order to\n(a) reliably distinguish signal from noise (about 85% validation score) and (b)\nreliably distinguish between EEG records collected from different human\nparticipants (about 80% validation score). Finally, we demonstrate that the\nproposed method works reliably even with an inexpensive (less than $100)\nconsumer-grade EEG sensing device and with participants who do not have\nprevious experience with EEG technology (EEG illiterate). All this in\ncombination opens up broad prospects for the development of new types of\nconsumer devices, [e.g.] based on virtual reality helmets or augmented reality\nglasses where EEG sensor can be easily integrated. The proposed method can be\nused to improve an online user experience by providing [e.g.] password-less\nuser identification for VR / AR applications. It can also find a more advanced\napplication in intensive care units where collected EEG data can be used to\nclassify the level of conscious awareness of patients during anesthesia or to\nautomatically detect hardware failures by classifying the input signal as\nnoise.","url_abs":"http://arxiv.org/abs/1708.01167v1","url_pdf":"http://arxiv.org/pdf/1708.01167v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"applying-advanced-machine-learning-models-to","repo_url":"https://github.com/yaricom/brainhash","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"},{"task_slug":"ssvep","task_name":"SSVEP"},{"task_slug":"user-identification","task_name":"User Identification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}