title: Dynamic frequency feature selection based approach for classification of motor imageries.
abstract: Electroencephalography (EEG) is one of the most popular techniques to record the brain activities such as motor imagery, which is of low signal-to-noise ratio and could lead to high classification error. Therefore, selection of the most discriminative features could be crucial to improve the classification performance. However, the traditional feature selection methods employed in brain-computer interface (BCI) field (e.g. Mutual Information-based Best Individual Feature (MIBIF), Mutual Information-based Rough Set Reduction (MIRSR) and cross-validation) mainly focus on the overall performance on all the trials in the training set, and thus may have very poor performance on some specific samples, which is not acceptable. To address this problem, a novel sequential forward feature selection approach called Dynamic Frequency Feature Selection (DFFS) is proposed in this paper. The DFFS method emphasized the importance of the samples that got misclassified while only pursuing high overall classification performance. In the DFFS based classification scheme, the EEG data was first transformed to frequency domain using Wavelet Packet Decomposition (WPD), which is then employed as the candidate set for further discriminatory feature selection. The features are selected one by one in a boosting manner. After one feature being selected, the importance of the correctly classified samples based on the feature will be decreased, which is equivalent to increasing the importance of the misclassified samples. Therefore, a complement feature to the current features could be selected in the next run. The selected features are then fed to a classifier trained by random forest algorithm. Finally, a time series voting-based method is utilized to improve the classification performance. Comparisons between the DFFS-based approach and state-of-art methods on BCI competition IV data set 2b have been conducted, which have shown the superiority of the proposed algorithm.
authors.affiliation: State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute, Xi׳an Jiaotong University, Xi׳an, Shaanxi, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Luo
authors.affiliation: State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute, Xi׳an Jiaotong University, Xi׳an, Shaanxi, China.
authors.forename: Zuren
authors.initials: Z
authors.lastname: Feng
authors.affiliation: State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute, Xi׳an Jiaotong University, Xi׳an, Shaanxi, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Zhang
authors.affiliation: State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute, Xi׳an Jiaotong University, Xi׳an, Shaanxi, China. Electronic address: lvna2009@mail.xjtu.edu.cn.
authors.forename: Na
authors.initials: N
authors.lastname: Lu
mesh_terms: D000465:Algorithms; D004569:Electroencephalography / Q000379:methods*; D001330:Electronic Data Processing / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D012984:Software
keywords: Brain computer interface; Classification; Feature selection; Motor imagery