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

Method for detecting driving fatigue based on electroencephalogram

A technology for EEG signals and driving fatigue, applied in diagnostic signal processing, electrical digital data processing, diagnostic recording/measurement, etc.

Active Publication Date: 2017-02-01
CHONGQING JINOU SCI & TECH DEV
View PDF1 Cites 33 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Support vector machine is one of the commonly used methods. Although it can achieve good results in practical applications, it needs to use the quadratic programming method to solve it. When the number of samples is relatively large, it needs to consume a lot of memory and computing time.

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
  • Method for detecting driving fatigue based on electroencephalogram
  • Method for detecting driving fatigue based on electroencephalogram
  • Method for detecting driving fatigue based on electroencephalogram

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0113] The present invention is further described above in conjunction with the accompanying drawings and specific embodiments.

[0114] (1) Select the feature quantities related to the fatigue driving state: feature selection refers to a feature set composed of a group of related feature quantities obtained after a series of corresponding processing on the original measurement signal, and then select some features from the feature set. The representative physical quantity with the best classification performance is used as a set of feature quantities to distinguish different behaviors, and then the relevant classification recognition method is used to separate different behaviors from the feature space by using the selected feature quantities. Therefore, for learning classification Both require training set samples:

[0115] T={(x 1 ,y 1 ),…,(x l ,y l )}∈(X×Y) l (1)

[0116] where x i ∈X=R n ,y i ∈Y=R,i=1,...,l;

[0117] For the training set that meets the requirem...

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 method for detecting driving fatigue based on electroencephalogram. The method specifically comprises the following steps: (1) selecting characteristic quantity related to a driving fatigue state; (2) performing electroencephalogram power spectrum analysis on the driving fatigue state; (3) performing electroencephalogram sample entropy analysis on the driving fatigue state; (4) performing electroencephalogram Kc complexity analysis on the driving fatigue state; (5) using support vector machine (SVM); (6) using least squares support vector machine (LS-SVM); (7) setting model training parameters (C and g) through a particle swarm optimization (PSO) algorithm. According to the method disclosed by the invention, electroencephalograms extracted in different driving states are respectively researched from a power spectrum angle by using related methods in non-linear dynamics, so that excellent effects can be achieved in accuracy and reliability.

Description

technical field [0001] The invention belongs to the technical field of traffic driving, in particular to a method for detecting driving fatigue based on electroencephalogram signals Background technique [0002] How to select the appropriate feature quantity from many features closely related to the fatigue state plays a very important role in the accurate identification of driving fatigue. After correlative processing of the EEG signal, the selected relevant feature vectors are obtained, and then some classifiers are used to make discrimination based on these feature vectors. Support vector machine is one of the commonly used methods. Although it can achieve good results in practical applications, it needs to use the quadratic programming method to solve it. When the number of samples is relatively large, it needs to consume a lot of memory and computing time. . The present invention studies the extracted EEG signals of different driving states from the perspective of pow...

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): G06F3/01G06K9/62A61B5/00
CPCG06F3/015A61B5/72G06F2203/011A61B2503/22G06F18/2411
Inventor 金纯
Owner CHONGQING JINOU SCI & TECH DEV
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