EEG signal classification method based on fast multidimensional empirical mode decomposition

Abstract

The invention discloses an EEG signal classification method based on fast multidimensional empirical mode decomposition. The method comprises: (1) collecting several groups of EEG signals and preprocessing the signals; (2) conducting the fast multidimensional empirical mode decomposition on the preprocessed signals to obtain all intrinsic mode function signals; (3) conducting spectrum analysis oneach layer of each intrinsic mode function signal and selecting a signal layer with the average power spectrum concentrated in the frequency bands of 8-12 Hz and 18-26 Hz as a new multidimensional signal; (4) making the new multidimensional signal passing through a spatial filter to extract characteristics of the EEG signals; (5) inputting the characteristics into a classifier for classification,selecting optimal parameters of a CSP according to the classification accuracy and classifying the EEG signals under different motion imaging tasks by utilizing the EEG characteristics under the optimal characteristics. The method solves the problems of pattern aliasing and low computing efficiency of a common multidimensional empirical mode decomposition algorithm, a decomposition result is moreinterpretable, and the classification accuracy of the EEG signals is improved.

Description

technical field [0001] The invention belongs to the fields of biomedical engineering and computers, and in particular relates to a method for classifying electroencephalogram signals based on fast multidimensional empirical mode decomposition. Background technique [0002] Existing studies have shown that when a person imagines a limb movement, the motor cortex area of ​​the brain related to the movement will have an electrophysiological response similar to that when the movement is performed, and the imaginary action potential will be induced. The preparation or planning of limb movements can cause a change in the activity state of a large number of nerve cells in the cortical motor center, resulting in the synchronous enhancement (event-related synchronization, ERS) or synchronous weakening (event-related desynchronization, ERD) of certain frequency components in the EEG signal. [0003] The analysis of EEG signals plays a very important role in neuroscience, psychology, b...

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Problems solved by technology

[0008] The present invention provides an EEG signal classification method based on fast multi-dimensional empirical mode decomposition, which solves the problems of common multi-dimensional empirical mode decomposition algorithm mode aliasing and low calculation efficiency, and the interpretability of the decomposition results is stronger, and to a large extent Improve the classification accuracy of EEG signals

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