Exploiting Task Constraints for Self-Calibrated Brain-Machine Interface Control Using Error-Related Potentials
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{"title"=>"Exploiting task constraints for self-calibrated brain-machine interface control using error-related potentials", "type"=>"journal", "authors"=>[{"first_name"=>"Iñaki", "last_name"=>"Iturrate", "scopus_author_id"=>"57194101260"}, {"first_name"=>"Jonathan", "last_name"=>"Grizou", "scopus_author_id"=>"55608034900"}, {"first_name"=>"Jason", "last_name"=>"Omedes", "scopus_author_id"=>"55567220200"}, {"first_name"=>"Pierre Yves", "last_name"=>"Oudeyer", "scopus_author_id"=>"6507418132"}, {"first_name"=>"Manuel", "last_name"=>"Lopes", "scopus_author_id"=>"56027423100"}, {"first_name"=>"Luis", "last_name"=>"Montesano", "scopus_author_id"=>"6602849539"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"isbn"=>"19326203 (ISSN)", "doi"=>"10.1371/journal.pone.0131491", "pui"=>"605565721", "sgr"=>"84939196766", "scopus"=>"2-s2.0-84939196766", "pmid"=>"26131890", "issn"=>"19326203"}, "id"=>"c5f67642-6edc-3e2f-815c-ac8674a94ac1", "abstract"=>"This paper presents a new approach for self-calibration BCI for reaching tasks using errorrelated potentials. The proposed method exploits task constraints to simultaneously calibrate the decoder and control the device, by using a robust likelihood function and an adhoc planner to cope with the large uncertainty resulting from the unknown task and decoder. The method has been evaluated in closed-loop online experiments with 8 users using a previously proposed BCI protocol for reaching tasks over a grid. The results show that it is possible to have a usable BCI control from the beginning of the experiment without any prior calibration. Furthermore, comparisons with simulations and previous results obtained using standard calibration hint that both the quality of recorded signals and the performance of the system were comparable to those obtained with a standard calibration approach. © 2015 Iturrate et al.", "link"=>"http://www.mendeley.com/research/exploiting-task-constraints-selfcalibrated-brainmachine-interface-control-using-errorrelated-potenti", "reader_count"=>17, "reader_count_by_academic_status"=>{"Student > Doctoral Student"=>1, "Researcher"=>3, "Student > Ph. D. Student"=>7, "Student > Master"=>4, "Student > Bachelor"=>1, "Lecturer"=>1}, "reader_count_by_user_role"=>{"Student > Doctoral Student"=>1, "Researcher"=>3, "Student > Ph. D. Student"=>7, "Student > Master"=>4, "Student > Bachelor"=>1, "Lecturer"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>8, "Agricultural and Biological Sciences"=>1, "Medicine and Dentistry"=>1, "Arts and Humanities"=>1, "Computer Science"=>5, "Neuroscience"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>8}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Neuroscience"=>{"Neuroscience"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Computer Science"=>{"Computer Science"=>5}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"United States"=>1, "Japan"=>1, "Algeria"=>1}, "group_count"=>0}

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{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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