Application of Quantum-behaved Particle Swarm Optimization to Motor imagery EEG Classification
Application of Quantum-behaved Particle Swarm Optimization to Motor imagery EEG Classification
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DOI：
10.1142/s0129065713500263
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发表时间：
2013-10
影响因子：
8
通讯作者：
Wei-Yen Hsu
中科院分区：
文献类型：
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作者：
Wei-Yen Hsu
在这项研究中，我们提出了一个识别系统的运动想象（MI）脑电图（EEG）数据的单次试验分析。该系统利用感觉运动皮层的事件相关脑电位（ERP）数据，主要包括自动消除伪影、特征提取、特征选择和分类。除了使用独立分量分析，提出了一种相似性度量，以进一步消除眼电（EOG）的伪影自动。几个潜在的功能，如小波分形特征，然后提取后续的分类。然后，使用量子行为粒子群优化算法（QPSO）从特征组合中选择特征。最后，选择的子特征进行分类的支持向量机（SVM）。与没有伪影消除，使用遗传算法（GA）的特征选择和特征分类与Fisher的线性判别（FLD）的MI数据从两个数据集的8个主题相比，结果表明，该方法是有前途的脑-机接口（BCI）的应用。
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.