EEG (electroencephalogram) signal feature classification method based on ABC-SVM

A technology for classification of EEG signals and features, applied to instruments, character and pattern recognition, computer components, etc., can solve problems such as low stability, falling into local optimal values, low efficiency, etc., and achieve improved classification recognition rate, The effect of high accuracy

Inactive Publication Date: 2016-09-07
HANGZHOU DIANZI UNIV
View PDF5 Cites 16 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, the commonly used model parameter optimization methods mainly include gradient descent method, genetic algorithm and particle swarm algorithm, etc., but these optimization methods have their own limitations.
For example, the gradient descent algorithm has low stability and low efficiency; the genetic algorithm has a sl...

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
  • EEG (electroencephalogram) signal feature classification method based on ABC-SVM
  • EEG (electroencephalogram) signal feature classification method based on ABC-SVM
  • EEG (electroencephalogram) signal feature classification method based on ABC-SVM

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0035] The present invention will be further described below in conjunction with accompanying drawing.

[0036] The present invention comprises the following steps:

[0037] Step 1. Use the CSP algorithm to extract the features of the EEG signal to obtain the sample feature vector f i ;

[0038] Step 2. utilize artificial bee colony algorithm to iteratively optimize the kernel function parameter and penalty factor of support vector machine;

[0039] Step 3. Use the optimal parameters optimized by the artificial bee colony algorithm to train the SVM classifier, and use the trained classifier to classify and predict the samples.

[0040] Wherein step 1, the EEG signal feature extraction obtains the feature vector and the specific steps are as follows:

[0041] The CSP algorithm is used to extract the features of the EEG signal, the number of channels of the experimental data is N, the number of sampling points of each channel is T, and the EEG data of one experiment is X n[N...

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 an EEG (electroencephalogram) signal feature classification method based on an ABC-SVM. The method comprises the steps: firstly carrying out EEG signal feature extraction through employing a CSP algorithm; secondly carrying out the optimization of a penalty factor C and a kernel parameter g of a SVM (support vector machine) through employing an artificial bee colony algorithm; finally carrying out the training of an SVM classifier through the obtained optimal parameter, and carrying out the classification prediction of samples through employing the trained classifier. Compared with an SVM classification recognition method optimized through a conventional algorithm, the method can effectively improve the classification recognition rate of an EEG signal, and is remarkably superior to a conventional classification recognition method.

Description

technical field [0001] The invention relates to a feature extraction and classification method of electroencephalogram signals, in particular to an ABC-SVM-based feature classification method of electroencephalogram signals. Background technique [0002] Brain-computer interface technology (Brain-computer Interface, BCI) is a new technology that integrates artificial intelligence, computer science and information, biomedical engineering and neuroscience. The field of cognitive science has a very broad application prospect. The characteristic of BCI technology is that it does not pass through the normal physiological pathways of peripheral nerves, muscles and bones of the human body, but allows the brain and external devices (computers or related instruments, etc.) to directly transmit information or control commands. [0003] Pattern classification of EEG signals is a particularly important part of the BCI system, and the classification performance is related to the real-ti...

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): G06K9/62
CPCG06F18/2411G06F18/214
Inventor 马玉良王振杰高云园武薇甘海涛
Owner HANGZHOU DIANZI 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