Extracting the nonlinear features of motor imagery EEG using parametric t-SNE

When performing studies on brain computer interface based rehabilitation problems, researchers frequently encounter difficulty due to the curse of dimensionality and the nonlinear nature of Motor Imagery Electroencephalography (MI-EEG). Though many approaches have been proposed recently to address the feature extraction problem and have shown surprising performance, unfortunately, most of them are non-parametric or linear dimension reduction techniques, which are limited in utility for out-of-sample extension for MI-EEG classification. To address the problem and obtain accurate MI-EEG features, a new unsupervised nonlinear dimensionality reduction technique termed parametric t-Distributed Stochastic Neighbor Embedding (P. t-SNE) is employed to extract the nonlinear features from MI-EEG. Considering that MI-EEG is a kind of non-stationary signal with remarkable time-frequency rhythmic distribution characteristics, Discrete Wavelet Transform (DWT) is used to extract the time-frequency features of MI-EEG. Furthermore, P. t-SNE is applied to selected wavelet components to get the nonlinear features. They are then combined serially to construct the feature vector. Experiments are conducted on a publicly available dataset, and the experimental results show that the nonlinear features have great visualization performance with obvious clustering distribution, and the feature extraction method indicates excellent classification performance as evaluated by a support vector machine classifier. This paper suggests a manifold based technique for further analysis and classification research of MI-EEG.