Artificial Bee Colony Algorithm for Single-Trial Electroencephalogram Analysis

• Published in: Clinical EEG and neuroscience Vol. 46; no. 2; pp. 119 - 125
• Main Authors: Hsu, Wei-Yen, Hu, Ya-Ping
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.04.2015; SAGE PUBLICATIONS, INC
• Subjects: Algorithms; Animals; Bees; Biomimetics - methods; Brain; Brain - physiology; Brain Mapping - methods; Brain-Computer Interfaces; Electroencephalography; Electroencephalography - methods; Evoked Potentials - physiology; Female; Humans; Male; Neurosciences; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Statistics as Topic; artificial bee colony (ABC); brain–computer interface (BCI); independent component analysis (ICA); support vector machine (SVM); autoregressive (AR) model; phase-locking value; electroencephalogram (EEG)
• ISSN: 1550-0594; 2169-5202
• DOI: 10.1177/1550059414538808

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.