Multi-lead correlation analysis electroencephalo-graph (EEG) feature extraction method

Abstract

The invention relates to a multi-lead correlation analysis electroencephalo-graph (EEG) feature extraction method. In multi-class motor imagery task recognition, EEG signal features of brain areas activated by a specific motor imagery task are effectively extracted, and effectively extracting the EEG signal features of the brain areas activated by the specific motor imagery task is a key problem to improve a recognition rate. With the multi-lead correlation analysis EEG feature extraction method, firstly multi-lead motor imagery EEG signals are extracted, then a correlation coefficient between every two lead EEG signals is analyzed to obtain a correlation parameter matrix, next a row variance of each correlation parameter matrix, the ratio values of the sum of all the row variances, and natural logarithms of all the row variances are calculated, obtained results are used as characteristic vectors of the EEG signals, and finally the characteristic vectors are input into a classifier to complete classifying recognition of multi-class motor imagery tasks. With the multi-lead correlation analysis EEG feature extraction method, not only can the EEG signal features of the brain areas activated by the specific motor imagery task at the same time can be fully extracted, influences on characteristic parameters can be reduced to a large extent, wherein the influences are caused by EEG signal individual differences, and further the problem that insufficience problem of electrode choosing can be solved.

Description

technical field [0001] The invention belongs to the field of electroencephalogram signal processing, and relates to an electroencephalogram signal feature extraction method, in particular to a feature extraction method for multi-lead motor imagery electroencephalogram signals. Background technique [0002] Electroencephalogram (electroencephalogram, EEG) is the potential change caused by the synaptic transmission signal of the cerebral cortex nerve cell group, which can reflect the brain's autonomous or induced consciousness activities, and is closely related to the actual action behavior. Since the German scientist Hans Berger recorded the electrical activity of the human brain in 1929, people have been trying to interpret human thinking activities through the identification of EEG signals. The brain-computer interface (BCI) based on it is considered to be an important milestone in the process of human understanding of the brain. BCI does not rely on the participation of m...

AI Technical Summary

Problems solved by technology

However, there are still some major problems in the study of motor imagery EEG: first, the resolution of EEG signals extracted through thinking tasks is low, especially for motor imagery tasks with small differences; second, increasing the types of recognition tasks will directly lead to recognition drop in accuracy

However, the current EEG feature extraction method only analyzes the information of a small number of channels. The benefits of doing so are obvious, requiring fewer electrodes, which not only shortens the preparation time, but also requires a small amount of information processing for a small amount of data.

Correspondingly, scholars such as Blankertz, Sannelli, Schroder, and Barachant pointed out that a small number of channels selected with neurophysiological prior knowledge does not necessarily produce better results than full-channel acquisition, and insufficient selection of electrodes will also reduce the classification accuracy.

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