Characteristic extraction method of motor imagery electroencephalogram signals

Abstract

The invention relates to a characteristic extraction method of motor imagery electroencephalogram signals. According to the method, firstly, the collected electroencephalogram signals are preprocessed, then EMD is performed on all leads of signals to obtain multi-order IMF signals, then the IMF signals which are identical in number of orders are selected as new signals, a spatial filter is obtained through a CSP algorithm, characteristics of the electroencephalogram signals are extracted and input into a classifier for classification, the optimal value of parameters in the EMD and CSP is selected according to the classification accuracy rate, and finally the electroencephalogram characteristics under the optimal parameter are obtained. Based on the EMD and CSP, characteristic extraction is performed on the motor imagery electroencephalogram signals, the signals can be decomposed into the multiple IMF signals in a self-adaptation mode according to characteristics of the electroencephalogram signals of different persons, characteristic extraction can be performed on the electroencephalogram signals only by few electrodes, and the classification accuracy rate of the electroencephalogram signals is increased to a greater degree.

Description

technical field [0001] The invention belongs to the technical field of EEG signal processing, and in particular relates to a method for extracting features of motor imagery EEG signals in a brain-computer interface (Brain-Computer Interface, BCI) system, which adopts a method combining empirical mode decomposition and common space mode Feature extraction of motor imagery EEG signals. Background technique [0002] Aging of the population and traffic accidents have caused a large number of patients with spinal cord lesions or injuries. The action commands of their brains cannot be transmitted to the muscles through normal internal pathways, thus losing the ability to move their limbs. Due to the advancement of modern medicine, these patients can continue to live in wheelchairs and beds for a long time, but they lose the ability to work and find it difficult to take care of themselves. This not only causes great pain for the patients, but also brings burdens to their families a...

AI Technical Summary

Problems solved by technology

However, the CSP algorithm has two shortcomings: first, the CSP algorithm has a significant effect on the EEG signals of a large number of electrodes, which limits its application in portable BCI systems; second, when performing motor imagery tasks, each subject The frequency bands in which ERS / ERD occurs have individual differences, and the CSP algorithm does not consider the individual differences of subjects and the frequency characteristics of EEG signals when extracting EEG features, which will result in low classification accuracy

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