title: Assembling A Multi-Feature EEG Classifier for Left-Right Motor Imagery Data Using Wavelet-Based Fuzzy Approximate Entropy for Improved Accuracy.
abstract: An EEG classifier is proposed for application in the analysis of motor imagery (MI) EEG data from a brain-computer interface (BCI) competition in this study. Applying subject-action-related brainwave data acquired from the sensorimotor cortices, the system primarily consists of artifact and background removal, feature extraction, feature selection and classification. In addition to background noise, the electrooculographic (EOG) artifacts are also automatically removed to further improve the analysis of EEG signals. Several potential features, including amplitude modulation, spectral power and asymmetry ratio, adaptive autoregressive model, and wavelet fuzzy approximate entropy (wfApEn) that can measure and quantify the complexity or irregularity of EEG signals, are then extracted for subsequent classification. Finally, the significant sub-features are selected from feature combination by quantum-behaved particle swarm optimization and then classified by support vector machine (SVM). Compared with feature extraction without wfApEn on MI data from two data sets for nine subjects, the results indicate that the proposed system including wfApEn obtains better performance in average classification accuracy of 88.2% and average number of commands per minute of 12.1, which is promising in the BCI work applications.
authors.affiliation: 1 Department of Information Management, National Chung Cheng University, No. 168, Sec. 1, University Rd., Min-Hsiung Township, Chiayi County 621, Taiwan.|2 Advanced Institute of Manufacturing with High-tech Innovations, National Chung Cheng University, No. 168, Sec. 1, University Rd. Min-Hsiung Township, Chiayi County 621, Taiwan.
authors.forename: Wei-Yen
authors.initials: WY
authors.lastname: Hsu
mesh_terms: D016477:Artifacts; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods*; D004585:Electrooculography; D019277:Entropy; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D007092:Imagination / Q000502:physiology*; D008297:Male; D009043:Motor Activity / Q000502:physiology*; D066191:Sensorimotor Cortex / Q000502:physiology*; D060388:Support Vector Machine; D058067:Wavelet Analysis
keywords: Brain–computer interface (BCI); electroencephalogram (EEG); fuzzy approximate entropy; motor imagery (MI); support vector machine (SVM)