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BioMed Research International
Volume 2016 (2016), Article ID 7051340, 11 pages
Research Article

Predicting Functional Recovery in Chronic Stroke Rehabilitation Using Event-Related Desynchronization-Synchronization during Robot-Assisted Movement

1Institute of Industrial Technology and Automation (ITIA), National Research Council (CNR), Via Bassini 15, 20133 Milan, Italy
2University of Brescia, Via Branze 38, 25123 Brescia, Italy
3Department of Neurophysiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133 Milan, Italy
4Villa Beretta Rehabilitation Center, Via Nazario Sauro 17, 23845 Costa Masnaga, Italy

Received 31 July 2015; Revised 20 November 2015; Accepted 23 November 2015

Academic Editor: Juan C. Moreno

Copyright © 2016 Marco Caimmi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Although rehabilitation robotics seems to be a promising therapy in the rehabilitation of the upper limb in stroke patients, consensus is still lacking on its additive effects. Therefore, there is a need for determining the possible success of robotic interventions on selected patients, which in turn determine the necessity for new investigating instruments supporting the treatment decision-making process and customization. The objective of the work presented in this preliminary study was to verify that fully robot assistance would not affect the physiological oscillatory cortical activity related to a functional movement in healthy subjects. Further, the clinical results following the robotic treatment of a chronic stroke patient, who positively reacted to the robotic intervention, were analyzed and discussed. First results show that there is no difference in EEG activation pattern between assisted and no-assisted movement in healthy subjects. Even more importantly, the patient’s pretreatment EEG activation pattern in no-assisted movement was completely altered, while it recovered to a quasi-physiological one in robot-assisted movement. The functional improvement following treatment was large. Using pretreatment EEG recording during robot-assisted movement might be a valid approach to assess the potential ability of the patient for recovering.