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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Veröffentlicht in:International journal of neural systems Jg. 23; H. 6; S. 1350026
1. Verfasser: Hsu, Wei-Yen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore 01.12.2013
Schlagworte:
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
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0129-0657
1793-6462
1793-6462
DOI:10.1142/S0129065713500263