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

Motion image electroencephalogram classification method based on channel weighting supporting vector

A motion imagery and support vector technology, applied in the field of pattern recognition, can solve the problems of difficulty in adaptive optimal channel selection, low recognition rate of multi-type motion imagery tasks, etc., to improve classification accuracy, reduce quantity, and improve classification accuracy Effect

Inactive Publication Date: 2014-05-28
夏津会盟建设工程有限公司
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a motor imagery EEG classification method based on channel weighted support vectors for the problems of low recognition rate of existing multi-type motor imagery tasks and difficulty in selecting the best adaptive channel.

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
  • Motion image electroencephalogram classification method based on channel weighting supporting vector
  • Motion image electroencephalogram classification method based on channel weighting supporting vector
  • Motion image electroencephalogram classification method based on channel weighting supporting vector

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0034] The motor imagery EEG classification method based on channel weighted support vectors of the present invention will be described in detail below in conjunction with the accompanying drawings. figure 1 for the implementation flow chart.

[0035] Such as figure 1 , the implementation of the method of the present invention mainly includes four steps: (1) obtaining multi-channel motor imagery EEG signals; (2) establishing a channel weight model; (3) constructing a two-class classification algorithm for channel weighted support vectors; (4) constructing channel Weighted support vector multiclass classification methods.

[0036] Each step will be described in detail below one by one.

[0037] Step 1: Obtain multi-channel motor imagery EEG signals

[0038] The 40 conductive electrode cap in the Scan4.3 acquisition device of American Neuro Scan Company was used to collect the EEG signals during the motor imagery process. The subjects sat in the wheelchair after wearing the ...

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 invention relates to a motion image electroencephalogram classification method based on a channel weighting supporting vector. According to the motion image electroencephalogram classification method, a multi-channel motion image electroencephalogram signal is obtained firstly, a weighting model of each channel is established on correlation analysis foundation between each pair of channel electroencephalogram signals secondly, the weighting model is embedded into an original optimizing problem of a channel weighting supporting vector machine for giving different weights to the input data from different channels thirdly, and a channel weighting supporting vector multi-class classification method is designed based on a two-class classification algorithm fourthly. With the adoption of the motion image electroencephalogram classification method, the channel selection can be automatically achieved, and the accuracy of the classification of a multi-motion image task is improved. The method has a wide application prospect in the field of brain-machine interfaces.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and relates to a method for pattern recognition of motor imagery EEG signals, in particular to a method for classifying multi-motion imagery tasks for the control of intelligent rehabilitation aids. Background technique [0002] As the center for controlling human thoughts, behaviors, emotions and other activities, the brain analyzes and processes information obtained from the external environment, and communicates with the outside world through neuromuscular pathways. However, many abnormal diseases, such as spinal cord injury, amyotrophic lateral sclerosis, and stroke, can damage or weaken the neural pathways that control muscles and the function of the muscles themselves. Severely ill patients may completely lose the ability to control themselves, and even affect functions such as speaking, completely unable to express their wishes or communicate with the outside world in traditional ways. ...

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
Inventor 佘青山罗志增马玉良席旭刚
Owner 夏津会盟建设工程有限公司
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