Feature extraction and visualization of MI-EEG by LLE algorithm

As a nonlinear time-varying and non-stationary signal, Motor Imagery Electroencephalography (MI-EEG) has attracted many researchers to use its time-frequency feature extraction by using Discrete Wavelet Transform (DWT) in brain computer interfaces (BCIs). Though a few people have devoted their efforts to exploring its nonlinear nature from the perspective of manifold learning, they hardly take into full account both time-frequency feature and nonlinear nature. To obtain features that can fully describe the information from a nonlinear nature and time-frequency perspective of MI-EEG, a novel feature extraction method is proposed based on the Locally Linear Embedding algorithm (LLE) and DWT. The multi-scale multi-resolution analysis is implemented for MI-EEG with DWT, and the valid time and frequency windows are determined in advance by a Wigner-Ville distribution. In view of the nonlinear structure in MI-EEG, LLE is applied to the approximation components to obtain the nonlinear features, and the statistics of the detail components are calculated to obtain the time-frequency features. After an organic combination of the two features, a Back-Propagation neural network optimized by a Genetic Algorithm was employed as a classifier to evaluate the effectiveness of the proposed feature extraction method. Compared with conventional DWT-based methods, the proposed method has a better effect on feature visualization with an obvious clustering distribution and improves the classification results and their stability. This paper successfully achieves manifold learning in signal processing of EEG.