title: Using Fractal and Local Binary Pattern Features for Classification of ECOG Motor Imagery Tasks Obtained from the Right Brain Hemisphere.
abstract: 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.
authors.affiliation: * School of Information Science and Engineering, Shandong University, Jinan 250100, P. R. China.|† School of Electrical Engineering and Automation, Qilu University of Technology, Jinan 250353, P. R. China.
authors.forename: Fangzhou
authors.initials: F
authors.lastname: Xu
authors.affiliation: * School of Information Science and Engineering, Shandong University, Jinan 250100, P. R. China.
authors.forename: Weidong
authors.initials: W
authors.lastname: Zhou
authors.affiliation: § Troops 72465 of PLA, Jinan 250022, P. R. China.
authors.forename: Yilin
authors.initials: Y
authors.lastname: Zhen
authors.affiliation: ǁ College of Physics and Electronic, Shandong Normal University, Jinan 250014, P. R. China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Yuan
authors.affiliation: ¶ Qilu Hospital, Shandong University, Jinan 250012, P. R. China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Wu
mesh_terms: D062207:Brain-Computer Interfaces; D066264:Datasets as Topic; D000069280:Electrocorticography / Q000379:methods*; D004828:Epilepsies, Partial / Q000503:physiopathology / Q000601:surgery; D005385:Fingers / Q000502:physiology; D017709:Fractals; D007839:Functional Laterality; D006801:Humans; D007092:Imagination / Q000502:physiology*; D016018:Least-Squares Analysis; D009044:Motor Cortex / Q000502:physiology* / Q000503:physiopathology / Q000601:surgery; D009483:Neuropsychological Tests; D010363:Pattern Recognition, Automated / Q000379:methods; D011597:Psychomotor Performance / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors; D014059:Tongue / Q000502:physiology
keywords: Local binary pattern; brain–computer interface; electrocorticogram; motor imagery