EEG intent decoding method based on non-negative cp decomposition model

A decoding method and EEG technology, applied in the fields of biological signal processing and pattern recognition, which can solve problems such as ignoring interactions

Active Publication Date: 2020-10-20
YANSHAN UNIV
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The extraction of characteristic parameters of EEG signals is of great significance to the diagnosis of neurological diseases. In the application of the traditional tensor discriminant analysis algorithm in the field of brain-computer interface, it usually focuses on extracting the frequency component of a single motor imagery EEG, and constructs The three-order tensor EEG data of time and time patterns realizes the optimized projection of different dimensions of EEG data including three patterns of time, frequency and space, and improves the recognition effect of motor imagery intentions. However, this is essentially an EEG feature. enhancement, ignoring the interaction between EEG modes

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 intent decoding method based on non-negative cp decomposition model
  • EEG intent decoding method based on non-negative cp decomposition model
  • EEG intent decoding method based on non-negative cp decomposition model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0032] Hereinafter, embodiments of the present invention will be described with reference to the drawings.

[0033] A kind of EEG intention decoding method based on non-negative CP decomposition model of the present invention, its overall flow chart is as follows figure 1 As shown, the method includes the following steps:

[0034] Step 1, truncate the EEG data successively, and select an 8-13Hz bandpass filter for filtering, and use the bandwidth parameter as f b = 2Hz complex Morlet wavelet to obtain the frequency components of EEG and construct fourth-order tensor data Wherein, c represents the channel, f represents the frequency, t represents the time, and s represents the experiment. In this embodiment, s is 120, including 60 left and right hand EEG data;

[0035] Using 5-fold cross-validation to select the test set and training set, the left and right hand EEG data are randomly divided into 5 groups along the direction of the experimental mode, and each group of data 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 present invention provides an EEG intent decoding method based on a non-negative CP decomposition model. The method extracts the time component features of the EEG of different subjects in the boundary avoidance task, optimizes the feature dimension by using 2-DPCA, and uses The support vector machine classifies the features, which can reflect the differences in the EEG of the subject in different states, and the accuracy of the EEG classification of a single subject is high; and this method uses the interaction between the various modes of the EEG, and uses the channel The components and frequency components obtain the time component characteristics of the EEG, and the characteristics of the obtained EEG time components are well separable. By optimizing its dimension, it can effectively detect the left and right hand movements in the boundary avoidance task. EEG intent to decode.

Description

technical field [0001] The invention relates to the fields of biological signal processing and pattern recognition, in particular to an EEG intention decoding method based on a non-negative CP decomposition model. Background technique [0002] Electroencephalogram is a graph obtained by amplifying the spontaneous biopotential of the brain from the scalp through sophisticated electronic instruments. It is the spontaneous and rhythmic electrical activity of brain cell groups recorded by electrodes. The evaluation of brain activity is of great significance, and it is an important clinical tool for studying the functional state of the brain, and for the diagnosis and detection of neurological diseases. In EEG research, a key step is to effectively extract and identify subject-specific characteristic parameters from EEG. [0003] In cognitive neurorehabilitation, "motor imagery" therapy is often used to improve the cognitive dysfunction of stroke patients. During the process of ...

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
CPCA61B5/7267A61B5/725A61B5/316A61B5/369G06F18/2411A61B5/374G16H50/20G16H50/70G06N20/10G06N20/00A61B5/726
Inventor 付荣荣于宝王世伟
Owner YANSHAN 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