Liu Rensong, Zhang Zhiwen, Duan Feng, Zhou Xin, Meng Zixuan
College of Computer and Control Engineering, Nankai University, Tianjin 300350, China.
Comput Intell Neurosci. 2017;2017:2727856. doi: 10.1155/2017/2727856. Epub 2017 Aug 9.
Motor imagery (MI) electroencephalograph (EEG) signals are widely applied in brain-computer interface (BCI). However, classified MI states are limited, and their classification accuracy rates are low because of the characteristics of nonlinearity and nonstationarity. This study proposes a novel MI pattern recognition system that is based on complex algorithms for classifying MI EEG signals. In electrooculogram (EOG) artifact preprocessing, band-pass filtering is performed to obtain the frequency band of MI-related signals, and then, canonical correlation analysis (CCA) combined with wavelet threshold denoising (WTD) is used for EOG artifact preprocessing. We propose a regularized common spatial pattern (R-CSP) algorithm for EEG feature extraction by incorporating the principle of generic learning. A new classifier combining the -nearest neighbor (KNN) and support vector machine (SVM) approaches is used to classify four anisomerous states, namely, imaginary movements with the left hand, right foot, and right shoulder and the resting state. The highest classification accuracy rate is 92.5%, and the average classification accuracy rate is 87%. The proposed complex algorithm identification method can significantly improve the identification rate of the minority samples and the overall classification performance.
运动想象（MI）脑电图（EEG）信号在脑机接口（BCI）中得到了广泛应用。然而，由于MI信号具有非线性和非平稳性的特点，其分类状态有限且分类准确率较低。本研究提出了一种基于复杂算法的新型MI模式识别系统，用于对MI EEG信号进行分类。在眼电图（EOG）伪迹预处理中，先进行带通滤波以获取与MI相关信号的频段，然后将典型相关分析（CCA）与小波阈值去噪（WTD）相结合用于EOG伪迹预处理。我们通过纳入泛化学习原理，提出了一种用于EEG特征提取的正则化公共空间模式（R-CSP）算法。一种结合k近邻（KNN）和支持向量机（SVM）方法的新型分类器用于对四种不同状态进行分类，即左手、右脚和右肩的想象运动以及静息状态。最高分类准确率为92.5%，平均分类准确率为87%。所提出的复杂算法识别方法能够显著提高少数样本的识别率和整体分类性能。
