title: Feature extraction for EEG-based brain-computer interfaces by wavelet packet best basis decomposition.
abstract: A method based on wavelet packet best basis decomposition (WPBBD) is investigated for the purpose of extracting features of electroencephalogram signals produced during motor imagery tasks in brain-computer interfaces. The method includes the following three steps. (1) Original signals are decomposed by wavelet packet transform (WPT) and a wavelet packet library can be formed. (2) The best basis for classification is selected from the library. (3) Subband energies included in the best basis are used as effective features. Three different motor imagery tasks are discriminated using the features. The WPBBD produces a 70.3% classification accuracy, which is 4.2% higher than that of the existing wavelet packet method.
authors.affiliation: School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China. ybh@sjtu.edu.cn
authors.forename: Bang-hua
authors.initials: BH
authors.lastname: Yang
authors.forename: Guo-zheng
authors.initials: GZ
authors.lastname: Yan
authors.forename: Rong-guo
authors.initials: RG
authors.lastname: Yan
authors.forename: Ting
authors.initials: T
authors.lastname: Wu
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D003201:Computers; D004569:Electroencephalography / Q000295:instrumentation* / Q000706:statistics & numerical data; D019277:Entropy; D005260:Female; D006801:Humans; D007092:Imagination / Q000502:physiology; D008297:Male; D009044:Motor Cortex / Q000502:physiology; D009048:Motor Skills; D009068:Movement / Q000502:physiology; D014584:User-Computer Interface