{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/109363"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/109363","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"EEG-based brain-computer interface for human-robot collaboration","abstract":"One of the expectations for the next-generation industrial robots is to work collaboratively with humans. Collaborative robots must be able to communicate with human collaborators intelligently and seamlessly. However, industrial robots in prevalence are not good at understanding human intentions and decisions. We propose to develop human-robot interactions based on Brain-Computer Interfaces (BCIs) transferring human cognition to robots directly. By collecting and encoding brain activities with BCIs, human can actively send commands to robots in thought or passively let robots monitor mental activities. We conduct two major experiments, i.e. BCI for welding robot and BCI for defective part picking robot, through which human operators can actively communicate with robots and work collaboratively on manufacturing tasks. The BCI for welding robot allows operators to select weld beads and command the robot to weld in thought. In the picking robot study, the robot picks defective part from a conveyor based on the decisions made when operators examining the qualities visually. Besides, to build faster and more accurate BCIs, we propose a Conv-CA model, which combines convolutional neural network (CNN) and canonical correlation analysis (CCA) to improve the performance of the state-of-art steady-state visually evoked potential (SSVEP) algorithm. We also conduct a study for passive BCI communication, i.e. the robot detects the circumstance when operators feel unsafe in the human-robot collaboration. When a fear response is detected, the robot can stop immediately to protect human safety.","abstract_html":"One of the expectations for the next-generation industrial robots is to work collaboratively with humans. Collaborative robots must be able to communicate with human collaborators intelligently and seamlessly. However, industrial robots in prevalence are not good at understanding human intentions and decisions. We propose to develop human-robot interactions based on Brain-Computer Interfaces (BCIs) transferring human cognition to robots directly. By collecting and encoding brain activities with BCIs, human can actively send commands to robots in thought or passively let robots monitor mental activities. We conduct two major experiments, i.e. BCI for welding robot and BCI for defective part picking robot, through which human operators can actively communicate with robots and work collaboratively on manufacturing tasks. The BCI for welding robot allows operators to select weld beads and command the robot to weld in thought. In the picking robot study, the robot picks defective part from a conveyor based on the decisions made when operators examining the qualities visually. Besides, to build faster and more accurate BCIs, we propose a Conv-CA model, which combines convolutional neural network (CNN) and canonical correlation analysis (CCA) to improve the performance of the state-of-art steady-state visually evoked potential (SSVEP) algorithm. We also conduct a study for passive BCI communication, i.e. the robot detects the circumstance when operators feel unsafe in the human-robot collaboration. When a fear response is detected, the robot can stop immediately to protect human safety.","abstract_has_math":false,"creators":["Li, Yao"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Systems & Entrepreneurial Engr","degree_department":null,"school":null,"contributors":["Kesavadas, Thenkurussi","Reis, Henrique M","Sreenivas, Ramavarapu S","Chowdhary, Girish"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-03-05T21:36:58Z","date_published":"2021-03-05T21:36:58Z","updated_at":"2026-07-22T22:24:50Z","subjects":["BCI","EEG","SSVEP","Industrial robot"],"languages":["en"],"rights":["Copyright 2020 Yao Li"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/109363","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kesavadas, Thenkurussi","Reis, Henrique M","Sreenivas, Ramavarapu S","Chowdhary, Girish"]},{"key":"dc:creator","label":"Author","values":["Li, Yao"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-03-05T21:36:58Z","2020-11-19","2020-12"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Systems & Entrepreneurial Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["BCI","EEG","SSVEP","Industrial robot"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Yao Li"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/109363"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["One of the expectations for the next-generation industrial robots is to work collaboratively with humans. Collaborative robots must be able to communicate with human collaborators intelligently and seamlessly. However, industrial robots in prevalence are not good at understanding human intentions and decisions. We propose to develop human-robot interactions based on Brain-Computer Interfaces (BCIs) transferring human cognition to robots directly. By collecting and encoding brain activities with BCIs, human can actively send commands to robots in thought or passively let robots monitor mental activities. We conduct two major experiments, i.e. BCI for welding robot and BCI for defective part picking robot, through which human operators can actively communicate with robots and work collaboratively on manufacturing tasks. The BCI for welding robot allows operators to select weld beads and command the robot to weld in thought. In the picking robot study, the robot picks defective part from a conveyor based on the decisions made when operators examining the qualities visually. Besides, to build faster and more accurate BCIs, we propose a Conv-CA model, which combines convolutional neural network (CNN) and canonical correlation analysis (CCA) to improve the performance of the state-of-art steady-state visually evoked potential (SSVEP) algorithm. We also conduct a study for passive BCI communication, i.e. the robot detects the circumstance when operators feel unsafe in the human-robot collaboration. When a fear response is detected, the robot can stop immediately to protect human safety.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-03-04 without embargo terms","The student, Yao Li, accepted the attached license on 2020-11-17 at 00:15.","The student, Yao Li, submitted this Dissertation for approval on 2020-11-17 at 00:15.","This Dissertation was approved for publication on 2020-11-19 at 16:41.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15892 on 2021-03-04 at 15:34:32","Made available in DSpace on 2021-03-05T21:36:58Z (GMT). No. of bitstreams: 3 LI-DISSERTATION-2020.pdf: 6363380 bytes, checksum: 44a97888273d8aeb71df76b092ea35a3 (MD5) LICENSE.txt: 4203 bytes, checksum: 72e9eab32f2cc49e258a74643bf8ef66 (MD5) PROQUEST_LICENSE.txt: 4549 bytes, checksum: b1add55270b9ccec015420d353ede756 (MD5) Previous issue date: 2020-11-19"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["EEG-based brain-computer interface for human-robot collaboration"]}]}],"canonical_facts":{"dc:contributor":["Kesavadas, Thenkurussi","Reis, Henrique M","Sreenivas, Ramavarapu S","Chowdhary, Girish"],"dc:creator":["Li, Yao"],"dc:date":["2021-03-05T21:36:58Z","2020-11-19","2020-12"],"dc:description":["One of the expectations for the next-generation industrial robots is to work collaboratively with humans. Collaborative robots must be able to communicate with human collaborators intelligently and seamlessly. However, industrial robots in prevalence are not good at understanding human intentions and decisions. We propose to develop human-robot interactions based on Brain-Computer Interfaces (BCIs) transferring human cognition to robots directly. By collecting and encoding brain activities with BCIs, human can actively send commands to robots in thought or passively let robots monitor mental activities. We conduct two major experiments, i.e. BCI for welding robot and BCI for defective part picking robot, through which human operators can actively communicate with robots and work collaboratively on manufacturing tasks. The BCI for welding robot allows operators to select weld beads and command the robot to weld in thought. In the picking robot study, the robot picks defective part from a conveyor based on the decisions made when operators examining the qualities visually. Besides, to build faster and more accurate BCIs, we propose a Conv-CA model, which combines convolutional neural network (CNN) and canonical correlation analysis (CCA) to improve the performance of the state-of-art steady-state visually evoked potential (SSVEP) algorithm. We also conduct a study for passive BCI communication, i.e. the robot detects the circumstance when operators feel unsafe in the human-robot collaboration. When a fear response is detected, the robot can stop immediately to protect human safety.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-03-04 without embargo terms","The student, Yao Li, accepted the attached license on 2020-11-17 at 00:15.","The student, Yao Li, submitted this Dissertation for approval on 2020-11-17 at 00:15.","This Dissertation was approved for publication on 2020-11-19 at 16:41.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15892 on 2021-03-04 at 15:34:32","Made available in DSpace on 2021-03-05T21:36:58Z (GMT). No. of bitstreams: 3 LI-DISSERTATION-2020.pdf: 6363380 bytes, checksum: 44a97888273d8aeb71df76b092ea35a3 (MD5) LICENSE.txt: 4203 bytes, checksum: 72e9eab32f2cc49e258a74643bf8ef66 (MD5) PROQUEST_LICENSE.txt: 4549 bytes, checksum: b1add55270b9ccec015420d353ede756 (MD5) Previous issue date: 2020-11-19"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/109363"],"dc:language":["en"],"dc:rights":["Copyright 2020 Yao Li"],"dc:subject":["BCI","EEG","SSVEP","Industrial robot"],"dc:title":["EEG-based brain-computer interface for human-robot collaboration"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Systems & Entrepreneurial Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:50Z"}