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

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.