000046928 001__ 46928
000046928 005__ 20210121114455.0
000046928 0247_ $$2doi$$a10.1038/srep13893
000046928 0248_ $$2sideral$$a93340
000046928 037__ $$aART-2015-93340
000046928 041__ $$aeng
000046928 100__ $$aIturrate, I.
000046928 245__ $$aTeaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control
000046928 260__ $$c2015
000046928 5060_ $$aAccess copy available to the general public$$fUnrestricted
000046928 5203_ $$aBrain-machine interfaces (BMI) usually decode movement parameters from cortical activity to control neuroprostheses. This requires subjects to learn to modulate their brain activity to convey all necessary information, thus imposing natural limits on the complexity of tasks that can be performed. Here we demonstrate an alternative and complementary BMI paradigm that overcomes that limitation by decoding cognitive brain signals associated with monitoring processes relevant for achieving goals. In our approach the neuroprosthesis executes actions that the subject evaluates as erroneous or correct, and exploits the brain correlates of this assessment to learn suitable motor behaviours. Results show that, after a short user € s training period, this teaching BMI paradigm operated three different neuroprostheses and generalized across several targets. Our results further support that these error-related signals reflect a task-independent monitoring mechanism in the brain, making this teaching paradigm scalable. We anticipate this BMI approach to become a key component of any neuroprosthesis that mimics natural motor control as it enables continuous adaptation in the absence of explicit information about goals. Furthermore, our paradigm can seamlessly incorporate other cognitive signals and conventional neuroprosthetic approaches, invasive or non-invasive, to enlarge the range and complexity of tasks that can be accomplished.
000046928 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/HYPER-CSD2009-00067$$9info:eu-repo/grantAgreement/ES/MINECO/DPI2010-21629-C02-01$$9info:eu-repo/grantAgreement/ES/MINECO/DPI2009-14732-C02-01$$9info:eu-repo/grantAgreement/EC/FP7/224631/EU/Tools for Brain-Computer Interaction/TOBI
000046928 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000046928 590__ $$a5.228$$b2015
000046928 591__ $$aMULTIDISCIPLINARY SCIENCES$$b7 / 62 = 0.113$$c2015$$dQ1$$eT1
000046928 592__ $$a2.034$$b2015
000046928 593__ $$aMultidisciplinary$$c2015$$dQ1
000046928 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000046928 700__ $$aChavarriaga, R.
000046928 700__ $$0(orcid)0000-0003-1183-349X$$aMontesano, L.$$uUniversidad de Zaragoza
000046928 700__ $$0(orcid)0000-0002-2957-0133$$aMinguez, J.$$uUniversidad de Zaragoza
000046928 700__ $$aMillán, J.D.R.
000046928 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000046928 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000046928 773__ $$g5 (2015), 13893 [10 pp.]$$pSci. rep.$$tScientific Reports$$x2045-2322
000046928 8564_ $$s830359$$uhttps://zaguan.unizar.es/record/46928/files/texto_completo.pdf$$yVersión publicada
000046928 8564_ $$s111933$$uhttps://zaguan.unizar.es/record/46928/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000046928 909CO $$ooai:zaguan.unizar.es:46928$$particulos$$pdriver
000046928 951__ $$a2021-01-21-10:48:08
000046928 980__ $$aARTICLE