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In particular, an operant-learning strategy is employed: while patients are performing the motor task they receive a feedback of their neural activity. If the latter agrees with the expected neurophysiological hypothesis, they are helped by the robotic arm in completing the task. The method trains patients to control the modulation of sensorimotor rhythms of their perilesional area and, at the same time, it should induce them to associate that modulation to the reaching movement. In this way, the modification of the neural activity becomes an alternative tool for controlling the impaired reaching ability bypassing the damaged brain area. Preliminary encouraging results were found in both the two first patients recruited in the program.<\/p>","DOI":"10.4018\/ijehmc.2014010107","type":"journal-article","created":{"date-parts":[[2014,6,25]],"date-time":"2014-06-25T12:11:50Z","timestamp":1403698310000},"page":"114-134","source":"Crossref","is-referenced-by-count":5,"title":["An EEG-Based BCI Platform to Improve Arm Reaching Ability of Chronic Stroke Patients by Means of an Operant Learning Training with a Contingent Force Feedback"],"prefix":"10.4018","volume":"5","author":[{"given":"Giulia","family":"Cisotto","sequence":"first","affiliation":[{"name":"Department of Information Engineering, University of Padua, Padova, Italy"}]},{"given":"Silvano","family":"Pupolin","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Padua, Padova, Italy"}]},{"given":"Marianna","family":"Cavinato","sequence":"additional","affiliation":[{"name":"Department of Neurophysiology, I.R.C.C.S. S. 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