Electroencephalogram classification method based on multi-classifier integration

A technology of multi-classifiers and classification methods, which is applied in the direction of instruments, electrical digital data processing, and pattern recognition in signals, etc., which can solve the problems of poor performance and difficulty in improving classification accuracy

Inactive Publication Date: 2017-06-06
SOUTHEAST UNIV
View PDF3 Cites 30 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] At present, a single classifier is mostly used in the pattern recognition of the brain-computer interface, which makes it difficult to improve the classification accuracy, and the general performance is also poor. Through the ensemble learning method, constructing and combining multiple classifiers, it is possible to obtain better results than a single classifier. High accuracy and pan-China performance

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
  • Electroencephalogram classification method based on multi-classifier integration
  • Electroencephalogram classification method based on multi-classifier integration
  • Electroencephalogram classification method based on multi-classifier integration

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0062] The present invention will be further described below in conjunction with the accompanying drawings.

[0063] Such as figure 1 Shown is an EEG classification method based on multi-classifier integration, and the specific implementation process is described.

[0064] Step 1: EEG signal acquisition and preprocessing

[0065] The EEG signals in the motor imagery mode of the subject were collected multiple times, and the EEG signals collected each time were processed by band-pass filtering to form a sample, and half of the samples were randomly selected from all samples as the training sample set. The remaining half of the samples are used as the test sample set.

[0066] The motor imagery EEG signals are collected through the electrode leads C3, Cz and C4 on the multi-channel collector, and the EEG electrodes are placed according to the international 10-20 system standard, such as figure 2 As shown, the sampling frequency is 128Hz; since the EEG signal of the Cz channe...

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 discloses an electroencephalogram signal classification based on multi-classifier integration. The method includes the following steps: performing electroencephalogram signal acquisition and preprocessing; performing feature extraction on electroencephalogram signals through utilization of a time domain analysis method, an autoregressive model and a discrete wavelet transform method in a combined manner; establishing an individual support vector machine classifier model, and converting outputs of the support vector machine classifiers into probability outputs; performing multi-classifier integrated electroencephalogram mode classification, and using a D-S evidence theory to fuse classified information of the three individual support vector machine classifier to obtain a final classification result. An experimental result shows that the method can improve the accuracy rate of motor imagery electroencephalogram signal classification.

Description

technical field [0001] The invention relates to the field of EEG signal processing and pattern recognition, in particular to the classification of motor imagery EEG signal patterns in brain-computer interfaces, and in particular to an EEG classification method based on multi-classifier integration. Background technique [0002] The brain-computer interface is a communication system for information transmission between the brain and external devices. It can convert the potential activity of the user's brain into control commands for external devices, thereby replacing body and language to communicate with the outside world. At present, motor imagery EEG collected from the human scalp is often used for non-invasive brain-computer interface control. The brain-computer interface based on motor imagery EEG mainly recognizes the user's motion intention through the analysis and processing of motor imagery EEG, and then converts the recognition results into control commands for exte...

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 Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06K9/48G06F3/01
CPCG06F3/015G06V10/46G06V10/478G06F2218/02G06F2218/08G06F2218/12G06F18/2411G06F18/254
Inventor 胡建中葛荣祥许飞云贾民平黄鹏
Owner SOUTHEAST 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