Detecting intention to walk in stroke patients from pre-movement EEG correlates

Sburlea, A.I. ; Montesano, L. (Universidad de Zaragoza) ; Cano de la Cuerda, R. ; Alguacil Diego, I.M. ; Miangolarra-Page, J. ; Minguez, J. (Universidad de Zaragoza)

Resumen: Background: 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.

Idioma: Inglés

DOI: 10.1186/s12984-015-0087-4

Año: 2015

Publicado en: Journal of NeuroEngineering and Rehabilitation 12, 1 (2015), [12 pp]
ISSN: 1743-0003

Factor impacto JCR: 2.419 (2015)

Categ. JCR: REHABILITATION rank: 9 / 65 = 0.138 (2015) - Q1 - T1
Categ. JCR: ENGINEERING, BIOMEDICAL rank: 25 / 76 = 0.329 (2015) - Q2 - T1
Categ. JCR: NEUROSCIENCES rank: 153 / 256 = 0.598 (2015) - Q3 - T2

Factor impacto SCIMAGO: 1.199 - Rehabilitation (Q1) - Health Informatics (Q1)

Financiación: info:eu-repo/grantAgreement/EC/FP7/270219/EU/Cognitive Control Framework for Robotic Systems/CORBYS
Financiación: info:eu-repo/grantAgreement/ES/MINECO/DPI2011-25892
Financiación: info:eu-repo/grantAgreement/ES/MINECO/HYPER-CSD2009-00067

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)
Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)