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Classification of right and left hand movement using phase space and recurrence quantification analysis

机译：利用相空间和递归量化分析对左右手运动进行分类

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Considering the non-stationary characteristics of Electroencephalogram (EEG) signals has a valuable impact in improving the classification rate for motor imagery tasks recognition. This paper aims to produce an efficient scheme for the classification of right and left hand movement. The scheme is based on combining linear and non-linear features in order to enhance the classification rate. Linear features are extracted from the amplitude frequency analysis (AFA) of the phase space of the EEG signal. While the non-linear features are extracted from a density matrix which is generated from the phase space of the signal and from the recurrence quantification analysis (RQA). We have used four classification approaches in this study; the linear discriminant analysis (LDA), support vector machine (SVM), Bayes and KNN classifiers. The Graz 2003 datasets has been used in this study. The maximal classification rate we have achieved is 90%. Results confirmed the robustness of the new technique and demonstrate its value as a classification approach in the field of brain computer interface BCI.
机译：考虑到脑电图（EEG）信号的非平稳特性对于提高运动图像任务识别的分类率具有重要的影响。本文旨在为左右手运动的分类提供一种有效的方案。该方案基于组合线性和非线性特征以提高分类率。从EEG信号的相空间的幅度频率分析（AFA）中提取线性特征。非线性特征是从密度矩阵中提取的，该密度矩阵是从信号的相空间和递归量化分析（RQA）中生成的。在这项研究中，我们使用了四种分类方法。线性判别分析（LDA），支持向量机（SVM），贝叶斯和KNN分类器。这项研究使用了Graz 2003数据集。我们实现的最大分类率为90％。结果证实了该新技术的鲁棒性，并证明了其在脑计算机接口BCI领域中作为一种分类方法的价值。

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