Electroencephalogram signal preprocessing method based on self-adapting noise cancellation system

An EEG signal and noise elimination technology, which is applied in applications, medical science, sensors, etc., can solve the problems of large amount of calculation, lack of adaptability, and poor real-time performance in solving the separation matrix, so as to improve accuracy and avoid eye-catching problems. Electric signal collection, the effect of reducing discomfort

Inactive Publication Date: 2018-11-20
XUZHOU NORMAL UNIVERSITY
View PDF15 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] At present, the commonly used methods for removing oculograph artifacts are: wavelet transform (WT) and independent component analysis (ICA), etc. WT is a typical time-frequency analysis method, but this method is limited by the wavelet basis function and the number of decomposition layers. The i

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 signal preprocessing method based on self-adapting noise cancellation system
  • Electroencephalogram signal preprocessing method based on self-adapting noise cancellation system
  • Electroencephalogram signal preprocessing method based on self-adapting noise cancellation system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0019] The following will be described in detail in conjunction with the accompanying drawings and specific embodiments.

[0020] A method for preprocessing EEG signals based on an adaptive noise cancellation system, comprising the steps of:

[0021] S1: First use the FastICA method to separate the collected EEG signals to obtain several independent components, calculate the kurtosis value of each component and automatically identify the electrooculogram component based on this value; then use the Mallat tower decomposition The algorithm performs L-level discrete wavelet decomposition on the electro-oculogram component to obtain an estimated electro-oculogram signal.

[0022] S2: Input the estimated electro-oculogram signal obtained in step 1 as the reference electro-oculogram signal of the adaptive noise cancellation system; process the reference electro-oculogram signal through an adaptive filter based on the recursive least squares method, Process the reference electro-ocu...

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 electroencephalogram signal preprocessing method based on a self-adapting noise cancellation system. In the method, fast independent component analysis (FastICA) and discrete wavelet transform (DWT) are used for estimating electronystagmogram signals in original electroencephalogram signals, wherein estimated electronystagmogram signals are used as reference electronystagmogram input signals of a self-adapting noise cancellation system (ANC); and a self-adapting filter and a weighting coefficient updating module are used for automatically removing electronystagmogram artifacts in the electroencephalogram signals, so that the automatic electronystagmogram signal component recognition, automatic parameter updating, and automatic electronystagmogram signal removalare achieved.

Description

technical field [0001] The invention relates to the field of physiological electroencephalogram signal processing, in particular to a method capable of realizing automatic identification of noise components, automatic updating of parameters and automatic removal of noise signals. Background technique [0002] Electroencephalogram (EEG) is an important bioelectrical signal of the human body, which contains a large amount of biological information of the human body. EEG signals change together with the cognitive process of the brain, enabling rapid responses to external stimuli. By monitoring the nerve electrical signals during the operation, feedback the changes in the integrity of the nerve function during the operation to the surgeon and the anesthesiologist, and find out the cause of the impaired cognitive function in time, so that they can take preventive measures to avoid irreversible damage and reduce the risk of surgery. risk of neurological deficits. [0003] Howeve...

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): A61B5/0476A61B5/04
CPCA61B5/7203A61B5/316A61B5/369
Inventor 吴玲玲余南南
Owner XUZHOU NORMAL UNIVERSITY
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