Changes in Electroencephalography Complexity using a Brain Computer Interface-Motor Observation Training in Chronic Stroke Patients: A Fuzzy Approximate Entropy Analysis.

ABSTRACT: Entropy-based algorithms have been suggested as robust estimators of electroencephalography (EEG) predictability or regularity. This study aimed to examine possible disturbances in EEG complexity as a means to elucidate the pathophysiological mechanisms in chronic stroke, before and after a brain computer interface (BCI)-motor observation intervention. Eleven chronic stroke subjects and nine unimpaired subjects were recruited to examine the differences in their EEG complexity. The BCI-motor observation intervention was designed to promote functional recovery of the hand in stroke subjects. Fuzzy approximate entropy (fApEn), a novel entropy-based algorithm designed to evaluate complexity in physiological systems, was applied to assess the EEG signals acquired from unimpaired subjects and st

SUBMITTER: Sun R 

PROVIDER: S-EPMC5591875 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature