Detecting intention to walk in stroke patients from pre-movement EEG correlates. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Sburlea, Andreea; Montesano, Luis; de la Cuerda, Roberto; Alguacil Diego, Isabel; Miangolarra-Page, Juan; Minguez, Javier
• Is Part Of: Journal of neuroengineering and rehabilitation. Volume 12:Issue 1(2015)
• Page Start: 1
• Page End: 12
• Publication Date: 2015-12
• Subjects: EEG -- Stroke -- Gait rehabilitation -- BCI -- MRCP -- ERD; Nervous system -- Diseases -- Patients -- Rehabilitation -- Periodicals; Nervous system -- Wounds and injuries -- Rehabilitation -- Periodicals; Biomedical engineering; 616.8043005
• DOI: 10.1186/s12984-015-0087-4
• Languages: English
• ISSNs: 1743-0003

Abstract

Abstract 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.