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.
Int J Neural Syst. 2013 Dec;23(6):1350026. doi: 10.1142/S0129065713500263. Epub 2013 Aug 22.
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.
在这项研究中，我们提出了一种用于单试分析运动想象 (MI) 脑电图 (EEG) 数据的识别系统。该系统应用于感觉运动皮层采集的事件相关脑电位 (ERP) 数据，主要包括自动伪迹消除、特征提取、特征选择和分类。除了使用独立成分分析外，还提出了一种相似性度量方法，以进一步自动去除眼电图 (EOG) 伪迹。然后提取几个潜在特征，如小波分形特征，用于后续分类。接下来，使用量子行为粒子群优化 (QPSO) 从特征组合中选择特征。最后，支持向量机 (SVM) 对选择的子特征进行分类。与不消除伪迹、使用遗传算法 (GA) 进行特征选择和使用 Fisher 线性判别 (FLD) 进行特征分类相比，从两个数据集的 8 个对象的 MI 数据来看，结果表明该方法在脑机接口 (BCI) 应用中具有广阔的应用前景。
