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

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. © 2015 sburlea et al.