{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/797"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/797","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Using consumer-grade brain-computer interface devices to capture and detect unaware facial recognitions","abstract":"The brain's natural reaction to viewing and processing faces in an aware manner is an area of research that has been explored for previously, however the brain's unaware reactions to these stimuli prove to be fairly less explored. An experiment was performed where recruited participants viewed images of individuals' faces while their brains' electroencephalography signals were recorded using a consumer-grade BCI device. The chosen images were assigned one of three classes of recognition, corresponding with what we expect the images to be recognized as: No Recognition, Possible Unaware Recognition, and Possible Aware Recognition. Using modern filtering and analysis techniques, it was found that, in effect, using consumer-grade brain-computer interface devices, the three previously-defined classes of recognition are easily identified, both with the human eye and machine learning tools, and previous efforts to detect unaware/subconscious facial recognition have been improved on using a variety of methods for data manipulation.","abstract_html":"The brain's natural reaction to viewing and processing faces in an aware manner is an area of research that has been explored for previously, however the brain's unaware reactions to these stimuli prove to be fairly less explored. An experiment was performed where recruited participants viewed images of individuals' faces while their brains' electroencephalography signals were recorded using a consumer-grade BCI device. The chosen images were assigned one of three classes of recognition, corresponding with what we expect the images to be recognized as: No Recognition, Possible Unaware Recognition, and Possible Aware Recognition. Using modern filtering and analysis techniques, it was found that, in effect, using consumer-grade brain-computer interface devices, the three previously-defined classes of recognition are easily identified, both with the human eye and machine learning tools, and previous efforts to detect unaware/subconscious facial recognition have been improved on using a variety of methods for data manipulation.","abstract_has_math":false,"creators":["Bellman, Christopher"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Science (MSc)","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Vargas Martin, Miguel"],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-08-01","date_published":"2017-08-01","updated_at":"2026-07-24T05:35:24Z","subjects":["BCI","EEG","Unaware","Facial","Recognition"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/797","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Vargas Martin, Miguel"]},{"key":"dc:creator","label":"Author","values":["Bellman, Christopher"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2017-08-18T14:32:24Z","2022-03-29T17:39:19Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2017-08-18T14:32:24Z","2022-03-29T17:39:19Z"]},{"key":"dc:date.issued","label":"Date","values":["2017-08-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MSc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["BCI","EEG","Unaware","Facial","Recognition"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/797"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The brain's natural reaction to viewing and processing faces in an aware manner is an area of research that has been explored for previously, however the brain's unaware reactions to these stimuli prove to be fairly less explored. An experiment was performed where recruited participants viewed images of individuals' faces while their brains' electroencephalography signals were recorded using a consumer-grade BCI device. The chosen images were assigned one of three classes of recognition, corresponding with what we expect the images to be recognized as: No Recognition, Possible Unaware Recognition, and Possible Aware Recognition. Using modern filtering and analysis techniques, it was found that, in effect, using consumer-grade brain-computer interface devices, the three previously-defined classes of recognition are easily identified, both with the human eye and machine learning tools, and previous efforts to detect unaware/subconscious facial recognition have been improved on using a variety of methods for data manipulation."]},{"key":"dc:title","label":"Title","values":["Using consumer-grade brain-computer interface devices to capture and detect unaware facial recognitions"]}]}],"canonical_facts":{"dc:contributor.advisor":["Vargas Martin, Miguel"],"dc:creator":["Bellman, Christopher"],"dc:date.accessioned":["2017-08-18T14:32:24Z","2022-03-29T17:39:19Z"],"dc:date.available":["2017-08-18T14:32:24Z","2022-03-29T17:39:19Z"],"dc:date.issued":["2017-08-01"],"dc:description.abstract":["The brain's natural reaction to viewing and processing faces in an aware manner is an area of research that has been explored for previously, however the brain's unaware reactions to these stimuli prove to be fairly less explored. An experiment was performed where recruited participants viewed images of individuals' faces while their brains' electroencephalography signals were recorded using a consumer-grade BCI device. The chosen images were assigned one of three classes of recognition, corresponding with what we expect the images to be recognized as: No Recognition, Possible Unaware Recognition, and Possible Aware Recognition. Using modern filtering and analysis techniques, it was found that, in effect, using consumer-grade brain-computer interface devices, the three previously-defined classes of recognition are easily identified, both with the human eye and machine learning tools, and previous efforts to detect unaware/subconscious facial recognition have been improved on using a variety of methods for data manipulation."],"dc:identifier.uri":["https://hdl.handle.net/10155/797"],"dc:language.iso":["en"],"dc:subject":["BCI","EEG","Unaware","Facial","Recognition"],"dc:title":["Using consumer-grade brain-computer interface devices to capture and detect unaware facial recognitions"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["Master of Science (MSc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:24Z"}