Related Experiment Video
Updated: Mar 29, 2026
Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
Published on: July 14, 2023
Detecting intention to walk in stroke patients from pre-movement EEG correlates
Andreea Ioana Sburlea1, Luis Montesano2, Roberto Cano de la Cuerda3
1Bit & Brain Technologies S.L., Calle Maria de Luna 11, nave 4, Zaragoza, 50018, Spain. andreea.sburlea@bitbrain.es.
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.
