Quantitative EEG for Predicting Upper Limb Motor Recovery in Chronic Stroke Robot-Assisted Rehabilitation.
Journal:
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Published Date:
Jul 1, 2017
Abstract
Stroke is a leading cause for adult disability, which in many cases causes motor deficits. Despite the developments in motor rehabilitation techniques, recovery of upper limb functions after stroke is limited and heterogeneous in terms of outcomes, and knowledge of important factors that may affect the outcome of the therapy is necessary to make a reasonable prediction for individual patients. In this paper, we assessed the relationship between quantitative electroencephalographic (QEEG) measures and the motor outcome in chronic stroke patients that underwent a robot-assisted rehabilitation program to evaluate the utility of QEEG indices to predict motor recovery. For this purpose, we acquired resting-state electroencephalographic signals from which the power ratio index (PRI), delta/alpha ratio, and brain symmetry index were calculated. The outcome of the motor rehabilitation was evaluated using upper limb section of the Fugl-Meyer Assessment. We found that PRI was significantly correlated with the motor recovery, suggesting that this index may provide useful information to predict the rehabilitation outcome.
Authors
Keywords
Adult
Aged
Aged, 80 and over
Diagnosis, Computer-Assisted
Electroencephalography
Equipment Design
Equipment Failure Analysis
Humans
Male
Middle Aged
Motion Therapy, Continuous Passive
Movement Disorders
Reproducibility of Results
Robotics
Sensitivity and Specificity
Stroke
Stroke Rehabilitation
Upper Extremity