Fatigue classification method for constructing brain function network and correlation vector machine based on generalized consistency

A brain function network and correlation vector machine technology is applied in the fatigue classification field of building brain function network and correlation vector machine based on generalized consistency. reliability, and the effect of improving the signal-to-noise ratio

Active Publication Date: 2019-05-21
WUYI UNIV
View PDF2 Cites 17 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the driving fatigue detection method based on power spectrum and entropy does not involve brain regional information, and cannot comprehensively and systematically study the mechanism of driving fatigue
However, in the construction of brain functional networks, functional connection networks (such as: phase lag index method, phase lock value method, etc...

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
  • Fatigue classification method for constructing brain function network and correlation vector machine based on generalized consistency
  • Fatigue classification method for constructing brain function network and correlation vector machine based on generalized consistency
  • Fatigue classification method for constructing brain function network and correlation vector machine based on generalized consistency

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0056] The specific embodiments of the present invention will be further described below in conjunction with the drawings:

[0057] Such as figure 1 As shown, a fatigue classification method based on generalized consistency to construct a brain function network and a correlation vector machine includes the following steps:

[0058] S1). Collect the EEG signals of the subjects during simulated driving through the wireless stem electrode EEG acquisition device, the duration is 90 minutes, the EEG data of 32 subjects are collected, and the EEG data of each subject is performed twice Signal acquisition, the first time as training data, the second time as testing. When collecting EEG signals, use the improved international 10-20 standard to place electrodes, a total of 24 leads, the electrode placement method is as follows figure 2 Shown.

[0059] S2) When the subject is performing simulated driving, the guided car on the screen will randomly issue a braking command, record the time i...

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 provides a fatigue classification method for constructing a brain function network and a correlation vector machine based on generalized consistency. Compared with the prior art, the method has higher reliability and accuracy, an effective fatigue classification network is constructed through information flowing direction and a causal relationship to classify connection characteristics of a brain network under different mental states, thereby effectively verifying results of topological structure research and improving the detection ability of driving fatigue. The fatigue classification method has the advantages that the brain network is constructed by using a generalized consistency algorithm method, the brain is regarded as a multi-regional cooperative network, informationcirculation direction and causal relationship between nodes of the brain are researched, topological structure changes of the brain network are analyzed under different mental states, the fatigue generation mechanism is disclosed, and a new perspective is provided for fatigue-related research; the correlation vector machine is used for classifying the connection characteristics, 90% or above classification accuracy can be achieved, the reliability of topological structure analysis is verified, and a new method is provided for fatigue detection.

Description

Technical field [0001] The invention relates to a fatigue classification method based on generalized consistency to construct a brain function network and a correlation vector machine. Background technique [0002] With the rapid economic development, automobiles have become the main means of transportation in people's lives. However, traffic safety has also become a problem that society urgently needs to solve. Fatigue driving is one of the important causes of major traffic accidents. Therefore, by studying the mechanism of the generation and induction of driving fatigue, detecting the physiological, psychological, and behavioral status of the driver, and making judgments on the fatigue degree of the subjects, it is beneficial to improve driving safety and reduce traffic accidents caused by fatigue. [0003] The physiological characteristics of the driver in a normal state are different from those in a fatigue state. Therefore, it is possible to determine whether the driver is i...

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
IPC IPC(8): A61B5/18A61B5/0476G06K9/00
CPCA61B5/18A61B5/369G06F18/00
Inventor 王洪涛刘旭程吴聪唐聪裴子安岳洪伟陈鹏李俊华
Owner WUYI UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap