000036764 001__ 36764
000036764 005__ 20210121114527.0
000036764 0247_ $$2doi$$a10.1186/s12984-015-0087-4
000036764 0248_ $$2sideral$$a93255
000036764 037__ $$aART-2015-93255
000036764 041__ $$aeng
000036764 100__ $$aSburlea, A.I.
000036764 245__ $$aDetecting intention to walk in stroke patients from pre-movement EEG correlates
000036764 260__ $$c2015
000036764 5060_ $$aAccess copy available to the general public$$fUnrestricted
000036764 5203_ $$aBackground: Most studies in the field of brain-computer interfacing (BCI) for lower limbs rehabilitation are carried out with healthy subjects, even though insights gained from healthy populations may not generalize to patients in need of a BCI. Methods: We investigate the ability of a BCI to detect the intention to walk in stroke patients from pre-movement EEG correlates. Moreover, we also investigated how the motivation of the patients to execute a task related to the rehabilitation therapy affects the BCI accuracy. Nine chronic stroke patients performed a self-initiated walking task during three sessions, with an intersession interval of one week. Results: Using a decoder that combines temporal and spectral sparse classifiers we detected pre-movement state with an accuracy of 64 % in a range between 18 % and 85.2 %, with the chance level at 4 %. Furthermore, we found a significantly strong positive correlation (r = 0.561, p = 0.048) between the motivation of the patients to perform the rehabilitation related task and the accuracy of the BCI detector of their intention to walk. Conclusions: We show that a detector based on temporal and spectral features can be used to classify pre-movement state in stroke patients. Additionally, we found that patients'' motivation to perform the task showed a strong correlation to the attained detection rate of their walking intention.
000036764 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/HYPER-CSD2009-00067$$9info:eu-repo/grantAgreement/ES/MINECO/DPI2011-25892$$9info:eu-repo/grantAgreement/EC/FP7/270219/EU/Cognitive Control Framework for Robotic Systems/CORBYS
000036764 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000036764 590__ $$a2.419$$b2015
000036764 591__ $$aREHABILITATION$$b9 / 65 = 0.138$$c2015$$dQ1$$eT1
000036764 591__ $$aENGINEERING, BIOMEDICAL$$b25 / 76 = 0.329$$c2015$$dQ2$$eT1
000036764 591__ $$aNEUROSCIENCES$$b153 / 256 = 0.598$$c2015$$dQ3$$eT2
000036764 592__ $$a1.199$$b2015
000036764 593__ $$aRehabilitation$$c2015$$dQ1
000036764 593__ $$aHealth Informatics$$c2015$$dQ1
000036764 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000036764 700__ $$0(orcid)0000-0003-1183-349X$$aMontesano, L.$$uUniversidad de Zaragoza
000036764 700__ $$aCano de la Cuerda, R.
000036764 700__ $$aAlguacil Diego, I.M.
000036764 700__ $$aMiangolarra-Page, J.
000036764 700__ $$0(orcid)0000-0002-2957-0133$$aMinguez, J.$$uUniversidad de Zaragoza
000036764 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000036764 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000036764 773__ $$g12, 1 (2015), [12 pp]$$pJournal of NeuroEngineering and Rehabilitation$$tJournal of NeuroEngineering and Rehabilitation$$x1743-0003
000036764 8564_ $$s2687464$$uhttps://zaguan.unizar.es/record/36764/files/texto_completo.pdf$$yVersión publicada
000036764 8564_ $$s11464$$uhttps://zaguan.unizar.es/record/36764/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000036764 909CO $$ooai:zaguan.unizar.es:36764$$particulos$$pdriver
000036764 951__ $$a2021-01-21-11:07:38
000036764 980__ $$aARTICLE