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

A multi-category motor imagery classification and recognition method

A technology of motor imagery and classification recognition, applied in the field of brain-computer interface, can solve the problems of reducing the classification ability of algorithms and increasing the burden of task processing

Active Publication Date: 2019-06-14
NORTHEASTERN UNIV LIAONING
View PDF8 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the bandwidth of its sub-bands still needs to be selected manually
The above methods are dedicated to improving the classification recognition accuracy of the binary classification. When dealing with multi-category motor imagery tasks, multiple binary classifiers are required, which increases the task processing burden and reduces the classification ability of the algorithm.

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 multi-category motor imagery classification and recognition method
  • A multi-category motor imagery classification and recognition method
  • A multi-category motor imagery classification and recognition method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0028] In order to better explain the present invention and facilitate understanding, the present invention will be described in detail below through specific embodiments in conjunction with the accompanying drawings.

[0029]This embodiment provides a multi-category motor imagery classification and recognition method, and its overall technical route is as follows: figure 1 As shown, it specifically includes the following two parts:

[0030] Subject selection section. The subjects were selected according to the principles of good health, normal vision and motor ability, no brain damage or neurological disease, similar age, and similar working conditions. In this embodiment, 9 subjects were selected for the test, male students aged 20-24 years old, healthy, with normal vision and motor ability, without brain damage or neurological disease.

[0031] In the part of test development and data processing, the following steps 1 to 7 are specifically performed for each subject.

[...

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 the field of brain-computer interface technology, and in particular to a multi-category motion image classification and recognition method. The multi-category motion image classification and recognition method is based on the singular value decomposition and the deep Boltzmann machine, uses the singular value decomposition algorithm to reduce and de-noise the motion imagining characteristic matrixes of each lead, and deeply abstracts the motion imaging features by the deep Boltzmann machine to extract the potential of sports imaging features. Compared with theprior art, the method can directly realize multi-category motion image recognition and can adaptively de-noise and significantly improve the accuracy of motion image recognition.

Description

technical field [0001] The present invention relates to the technical field of Brain-computer Interface (BCI), in particular to a multi-category motor imagery classification and recognition method, and more specifically, to a multi-class recognition method based on singular value decomposition and deep Boltzmann machine. Classification method for motor imagery classification. Background technique [0002] Statistics show that every 6 seconds around the world, one person has a stroke, and one person dies of a stroke every 20 seconds. Due to the high morbidity, high mortality, high disability rate, and high recurrence rate of stroke, the medical community regards stroke as one of the three major diseases that threaten human health together with coronary heart disease and cancer. Brain rehabilitation is based on the plasticity of the brain. Through specific training equipment and means, patients with brain injury can relearn to restore daily limb motor function. Traditional r...

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): A61B5/0476A61B5/00G06K9/62
Inventor 于忠亮宋锦春
Owner NORTHEASTERN UNIV LIAONING
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