Artificial bee colony algorithm for single-trial electroencephalogram analysis.
In this study, we propose an analysis system combined with feature selection to further improve the classification accuracy of single-trial electroencephalogram (EEG) data. Acquiring event-related brain potential data from the sensorimotor cortices, the system comprises artifact and background noise removal, feature extraction, feature selection, and feature classification. First, the artifacts and background noise are removed automatically by means of independent component analysis and surface Laplacian filter, respectively. Several potential features, such as band power, autoregressive model, and coherence and phase-locking value, are then extracted for subsequent classification. Next, artificial bee colony (ABC) algorithm is used to select features from the aforementioned feature combination. Finally, selected subfeatures are classified by support vector machine. Comparing with and without artifact removal and feature selection, using a genetic algorithm on single-trial EEG data for 6 subjects, the results indicate that the proposed system is promising and suitable for brain-computer interface applications.
Department of Information Management, National Chung Cheng University, Chiayi, Taiwan Advanced Institute of Manufacturing with High-tech Innovations, National Chung Cheng University, Chiayi, Taiwan shenswy@gmail.com shen@csie.ncku.edu.tw.
Wei-Yen
WY
Hsu
Department of Information Management, National Chung Cheng University, Chiayi, Taiwan.
Ya-Ping
YP
Hu
D000465:Algorithms; D000818:Animals; D001516:Bees; D032701:Biomimetics / Q000379:methods; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology; D005260:Female; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013223:Statistics as Topic
artificial bee colony (ABC); autoregressive (AR) model; brain–computer interface (BCI); electroencephalogram (EEG); independent component analysis (ICA); phase-locking value; support vector machine (SVM)