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APPLICATION OF QUANTUM-BEHAVED PARTICLE SWARM OPTIMIZATION TO MOTOR IMAGERY EEG CLASSIFICATION

WEI-YEN HSU

Department of Information Management, Advanced Institute of Manufacturing with High-tech Innovations, National Chung Cheng University, No. 168, Sec. 1, University Rd., Min-Hsiung Township, Chia-yi County 621, Taiwan

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https://doi.org/10.1142/S0129065713500263Cited by:36

Abstract

In this study, we propose a recognition system for single-trial analysis of motor imagery (MI) electroencephalogram (EEG) data. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the system chiefly consists of automatic artifact elimination, feature extraction, feature selection and classification. In addition to the use of independent component analysis, a similarity measure is proposed to further remove the electrooculographic (EOG) artifacts automatically. Several potential features, such as wavelet-fractal features, are then extracted for subsequent classification. Next, quantum-behaved particle swarm optimization (QPSO) is used to select features from the feature combination. Finally, selected sub-features are classified by support vector machine (SVM). Compared with without artifact elimination, feature selection using a genetic algorithm (GA) and feature classification with Fisher's linear discriminant (FLD) on MI data from two data sets for eight subjects, the results indicate that the proposed method is promising in brain–computer interface (BCI) applications.

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Published: 21 August 2013

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Accepted 3 July 2013

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APPLICATION OF QUANTUM-BEHAVED PARTICLE SWARM OPTIMIZATION TO MOTOR IMAGERY EEG CLASSIFICATION

Abstract

References

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