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A classifier is trained by using the gradient boosting in conjunction with ordinary least squares (OLS) method. The fractal intercept, lacunarity and LBP features are extracted to classify imagined movements of either the left small finger or the tongue. Experimental results on dataset I of BCI competition III demonstrate the superior performance of our method. The cross-validation accuracy and accuracy is 90.6% and 95%, respectively. Furthermore, the low computational burden of this method makes it a promising candidate for real-time BCI systems. <\/jats:p>","DOI":"10.1142\/s0129065716500222","type":"journal-article","created":{"date-parts":[[2016,3,30]],"date-time":"2016-03-30T00:08:42Z","timestamp":1459296522000},"page":"1650022","source":"Crossref","is-referenced-by-count":37,"title":["Using Fractal and Local Binary Pattern Features for Classification of ECOG Motor Imagery Tasks Obtained from the Right Brain Hemisphere"],"prefix":"10.1142","volume":"26","author":[{"given":"Fangzhou","family":"Xu","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Shandong University, Jinan 250100, P. R. China"},{"name":"School of Electrical Engineering and Automation, Qilu University of Technology, Jinan 250353, P. R. 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