{"id":{"repo_id":"uts","oai_identifier":"oai:opus.lib.uts.edu.au:10453/189917"},"canonical_url":"https://search.dev.ndltd.org/etd/uts/oai:opus.lib.uts.edu.au:10453/189917","repository":{"repo_id":"uts","name":"University of Technology Sydney","base_url":"https://opus.lib.uts.edu.au/oai/request"},"display":{"title":"A multi-level brain-computer interface for object recognition and object identification","abstract":"This research introduces an innovative BCI system designed to decode a user's intention towards an object, determining whether it pertains to object recognition or identification, and then channel the data to an algorithm trained specifically for those tasks. It consists of two principal components: one algorithm crafted to differentiate between tasks related to object recognition or identification and another set of algorithms trained to categorize objects for both recognition and identification purposes. The process begins with the input of one-second pre-processed EEG data related to an object. This data first passes through the distinguishing algorithm to ascertain the user's intention regarding the observed object, determining whether the task involves object recognition or identification. Once the user's intent is identified, the data is forwarded to the relevant algorithm, which classifies the object into its respective category fitted to the specific task at hand. The system's output then informs the user of their cognitive task (recognition or identification) and the category of the object they were observing.","abstract_html":"This research introduces an innovative BCI system designed to decode a user's intention towards an object, determining whether it pertains to object recognition or identification, and then channel the data to an algorithm trained specifically for those tasks. It consists of two principal components: one algorithm crafted to differentiate between tasks related to object recognition or identification and another set of algorithms trained to categorize objects for both recognition and identification purposes. The process begins with the input of one-second pre-processed EEG data related to an object. This data first passes through the distinguishing algorithm to ascertain the user's intention regarding the observed object, determining whether the task involves object recognition or identification. Once the user's intent is identified, the data is forwarded to the relevant algorithm, which classifies the object into its respective category fitted to the specific task at hand. The system's output then informs the user of their cognitive task (recognition or identification) and the category of the object they were observing.","abstract_has_math":false,"creators":["Leong, Daniel"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024","date_published":"2024","updated_at":"2026-07-24T06:32:12Z","subjects":[],"languages":["en"],"rights":["info:eu-repo/semantics/openAccess","The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. 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It consists of two principal components: one algorithm crafted to differentiate between tasks related to object recognition or identification and another set of algorithms trained to categorize objects for both recognition and identification purposes. The process begins with the input of one-second pre-processed EEG data related to an object. This data first passes through the distinguishing algorithm to ascertain the user's intention regarding the observed object, determining whether the task involves object recognition or identification. Once the user's intent is identified, the data is forwarded to the relevant algorithm, which classifies the object into its respective category fitted to the specific task at hand. 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