Journal
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
Volume 26, Issue 6, Pages -Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0129065716500222
Keywords
Local binary pattern; brain-computer interface; electrocorticogram; motor imagery
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Funding
- Key Program of the Natural Science Foundation of Shandong Province [ZR2013FZ002]
- Program of Science and Technology of Suzhou [ZXY2013030]
- Development Program of Science and Technology of Shandong [2014GSF118100]
- Fundamental Research Funds of Shandong University [2014QY008]
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The feature extraction and classification of brain signal is very significant in brain-computer interface (BCI). In this study, we describe an algorithm for motor imagery (MI) classification of electrocorticogram (ECoG)-based BCI. The proposed approach employs multi-resolution fractal measures and local binary pattern (LBP) operators to form a combined feature for characterizing an ECoG epoch recording from the right hemisphere of the brain. 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.
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