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Motor imagery electroencephalogram feature extraction method based on matrix variable Gaussian model

A technology of motion imagination and Gaussian model, applied in the direction of mechanical mode conversion, electrical digital data processing, character and pattern recognition, etc., can solve the problems of insufficient precision, achieve the effect of improving classification accuracy and improving utilization rate

Active Publication Date: 2020-07-03
HANGZHOU DIANZI UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to overcome the defect of insufficient precision of the motor imagery EEG feature extraction method in the prior art, and provide a motor imagery EEG feature extraction method based on matrix variable Gaussian model and two-dimensional discrimination local retention

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  • Motor imagery electroencephalogram feature extraction method based on matrix variable Gaussian model
  • Motor imagery electroencephalogram feature extraction method based on matrix variable Gaussian model
  • Motor imagery electroencephalogram feature extraction method based on matrix variable Gaussian model

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Embodiment Construction

[0047] The present invention will be further described below in conjunction with accompanying drawing.

[0048] A motor imagery EEG feature extraction method based on a matrix variable Gaussian model, the specific steps are as follows:

[0049] Step 1. EEG tests are performed on multiple testers, and a proposed sample set and an expanded sample set are established; each tester performs motor imagery during the test. There are Z types of motor imagery. In this embodiment, Z=4; the four kinds of motor imagination are respectively moving the left hand, moving the right hand, moving the feet, and moving the tongue. Each sample in the proposed sample set is divided into Z categories according to the difference in motor imagery. The proposed sample set is divided into a training sample set and a test sample set. The training sample set is expressed as x=(x 1 ,x 2 ,...,x n ). There are n samples in the training sample set. Set the value of the parameter t to 1. Set dimension...

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Abstract

The invention discloses a motor imagery electroencephalogram feature extraction method based on a matrix variable Gaussian model. In the prior art, a motor imagery electroencephalogram feature extraction method is insufficient in precision. The method comprises the following steps: 1, performing electroencephalogram test to establish a sample set; 2, performing filter bank common space modal operation on the training sample set x; 3, constructing an inter-class weight matrix and an intra-class weight matrix; 4, calculating an intra-class space covariance matrix and an intra-class frequency covariance matrix; and 5, splitting the inter-class scatter matrix. 6, establishing a projection matrix. 7, calculating a characteristic number pair; and 8, obtaining d-dimensional features for training.9, training an SVM model. And 10, detecting and identifying the motor imagery of the detected person. The conventional processing method ignores the spatial information in the electroencephalogram signal. Matrix dimension reduction processing is used, the thought of a matrix variable Gaussian model is introduced, and the utilization rate of space information is further increased.

Description

technical field [0001] The invention belongs to the technical field of electroencephalogram signal classification, in particular to a motor imagery electroencephalogram feature extraction method based on a matrix variable Gaussian model and two-dimensional discriminant local retention. Background technique [0002] EEG bioelectrical signal is an important input signal source in wearable diagnosis and treatment system. In-depth research on the processing of EEG signals is of great significance for understanding pathological mechanisms and exploring new methods for disease diagnosis and treatment. The classification and processing of motor imagery EEG signals can assist patients in the recovery of sports injuries, and even use the human brain to control external equipment to realize sports and make up for the regrets of the body. [0003] How to accurately classify different motor imagery EEG signals, researchers have proposed many methods, but there are still many problems t...

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Application Information

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IPC IPC(8): G06K9/62A61B5/0476A61B5/00G06F3/01
CPCA61B5/725A61B5/7267G06F3/015A61B5/316A61B5/369G06F18/21322G06F18/2411G06F18/214
Inventor 祝磊杨君婷胡奇峰
Owner HANGZHOU DIANZI UNIV
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