{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/24378"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/24378","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A stochastic control framework for the design of observational brain-computer interfaces based on human error potentials","abstract":"Brain-computer interfaces (BCI) allow human subjects to interact with exogenous systems through neural signals. The goal of this interaction may be to induce behaviors or properties in either the exogenous system or the human subject. In this thesis, we develop a novel framework for designing BCIs based on principles from adaptive control. In particular, we exploit scalp electroencephalography-derived correlates of the human error processing system to recover a subject’s desired policy for the exogenous system. 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Further, we discuss the application of this framework to building co-adaptive BCIs and as a tool for understanding the learning process during BCI interaction.","abstract_has_math":false,"creators":["Steines, David A."],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Coleman, Todd P."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-05-25T14:37:19Z","date_published":"2011-05-25T14:37:19Z","updated_at":"2026-07-22T22:25:23Z","subjects":["Brain-computer interface","feedback-related negativity","Bayesian control rule"],"languages":["en"],"rights":["Copyright 2011 David A. 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