Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

A Classification Method of Elm Motor Imagery EEG Based on AR Coefficient Space

A technology of motion imagery and classification method, applied in the direction of instruments, character and pattern recognition, computer parts, etc., can solve the problem of taking up a lot of time, and achieve the effect of high stability, good generalization performance and fast learning speed

Inactive Publication Date: 2018-07-17
FUZHOU UNIV
View PDF2 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In the traditional artificial neural network, the hidden layer node parameters of the network are solved by iterative algorithm for multiple optimizations, and these iterative steps take up a lot of time in the training process

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A Classification Method of Elm Motor Imagery EEG Based on AR Coefficient Space
  • A Classification Method of Elm Motor Imagery EEG Based on AR Coefficient Space
  • A Classification Method of Elm Motor Imagery EEG Based on AR Coefficient Space

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0040] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0041] like figure 1 As shown, the present embodiment provides an ELM motor imagery EEG classification method based on the AR coefficient space, which specifically includes the following steps:

[0042] Step S1: Use the p-order AR model to fit single-channel motor imagery EEG signals, using the formula Represents; where, x(n) represents the nth sampling value of the signal, p is the order of the AR model, a k is the AR coefficient of the AR model, k=1,2,...,p, s(n) is the mean value is zero, and the variance is σ 2 The white noise residual;

[0043] Step S2: Use the Burg algorithm to determine the undetermined coefficient a of the AR model described in step S1 1 ,a 2 ,...,a p , to solve;

[0044] Step S3: Connect the p-order AR coefficients of the motor imagery EEG signals of the m channels into a row vector, as follows: a i =[a i1 ,a i2 ,a i3...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The present invention relates to an ELM motor imagery EEG classification method based on the AR coefficient space. First, the motor imagery EEG signal of a single channel is used as a random signal, and the p-order AR model is used for fitting, expressed as: ; and then the Burg algorithm is used Solve the undetermined coefficients a1, a2, ..., ap of the AR model; then combine the p-order AR coefficients of m channel motor imagery EEG signals into an AR coefficient vector, and embed it into the original optimization problem of ELM network parameter training Among them, the optimal external weight β is solved, and the ELM classification algorithm based on the AR coefficient space is constructed. The method of the invention improves the classification accuracy and classification speed of motor imagery EEG signals.

Description

technical field [0001] The invention relates to the field of pattern recognition of EEG signals, in particular to an ELM motor imagery EEG classification method based on AR coefficient space. Background technique [0002] The brain is the high-level nerve center of the human body. Because of its complexity and the rich diversity of neural connections, the research on the human brain involves multi-field interdisciplinary technologies and has become one of the hot spots in the development direction of contemporary science. [0003] In today's society, diseases caused by neuromuscular and brain disorders are affecting people's quality of life. However, in medicine, these diseases can only be alleviated through various methods at present, but they cannot be cured. However, these patients are eager to communicate with others normally, so the application of brain-computer interface was born. Brain-computer interface is a new type of channel for information exchange and control ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
CPCG06F18/2411
Inventor 黄志华林苏云郭顺英文宇坤
Owner FUZHOU UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com