The invention provides a DWT- and Parametric t-SNE-based characteristic extracting method of
motor imagery EEG(Electroencephalogram) signals. First,
effective time and frequency ranges of EEG characteristics are determined by using a Wigner-Ville distribution and power spectrum; the EEG signals in a
specific time and frequency segment is subjected to three-layer discrete
wavelet decomposition and statistical characteristic quantity including the average value, the energy average value, the
mean square error and the like are calculated and are taken as the time
frequency characteristic of the EEG signals; at the same time, a parameterization t-SNE
algorithm is utilized for performing non-linear characteristic mapping on said
wavelet coefficients and embedded coordinates corresponding to a low-dimensional space are taken as the non-linear characteristic; the two characteristics are standardized and a characteristic vector including both the time frequency information and the non-linear information of the EEG signals in the
specific time frequency segment is obtained. According to the invention, EEG characteristics of compactness and completeness are obtained and a method for solving a problem of poor generalization performance of a traditional manifold learning
algorithm in pattern classification application through fitting a multilayer
forward propagation neural network to nonlinear mapping is proposed, so that accuracy of pattern classification of MI-EEG signals is improved further.