Application of quantum-behaved particle swarm optimization to motor imagery EEG classification

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...

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Bibliographic Details
Published in:International journal of neural systems Vol. 23; no. 6; p. 1350026
Main Author: Hsu, Wei-Yen
Format: Journal Article
Language:English
Published: Singapore 01.12.2013
Subjects:
Algorithms
Brain-Computer Interfaces
Electroencephalography
Humans
Imagination - physiology
Quantum Theory
Signal Processing, Computer-Assisted
Somatosensory Cortex - physiology
Support Vector Machine
ISSN:0129-0657, 1793-6462, 1793-6462
Online Access:Get more information
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Summary: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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content type line 23
ISSN:0129-0657
1793-6462
1793-6462
DOI:10.1142/S0129065713500263