Self-adaptive EEG signal ocular artifact automatic removal method

A technology of electroencephalogram signal and electro-oculography artifact, applied in the field of biological information, can solve problems such as manual screening, and achieve the effect of automatic removal of electro-oculogram artifact

Inactive Publication Date: 2010-10-27
BEIJING UNIV OF TECH
View PDF0 Cites 74 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The invention solves the problem of manual screening of empirical modal components containing electrooculogram artifacts, thereby achieving the purpose of automatically removing electrooculogram artifacts from electroencephalogram signals

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
  • Self-adaptive EEG signal ocular artifact automatic removal method
  • Self-adaptive EEG signal ocular artifact automatic removal method
  • Self-adaptive EEG signal ocular artifact automatic removal method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0051] The method for removing ocular artifacts in EEG signals based on HHT, the complete process of signal processing includes the following four (1.2.3.4) parts. Among them, the first part is the existing method, and the features of the present application include three (2.3.4) parts:

[0052] The complete process of signal processing such as figure 1 Shown.

[0053] 1. Empirical Modal Decomposition (EMD) of EEG signals.

[0054] The EEG signal X(t) is a non-stationary signal. In order to study its transient characteristics, empirical mode decomposition (EMD) must be performed to obtain each IMF component c i (t) and the remainder r(t).

[0055] Among them, i is the number of IMF components obtained by decomposition, i is completely driven by data, and the value of i is completely determined by X(t). For different X(t) signals, the number of IMF components obtained by decomposition i is Different, this also reflects the adaptability of EMD decomposition. This is different from wa...

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 provides a self-adaptive EEG signal ocular artifact automatic removal method, which belongs to the technical field of biological information and is mainly used in a pretreatment process for acquiring an EEG signal. The method comprises: performing real-time empirical mode decomposition (EMD) of collected EEG data having ocular artifacts; performing Hilbert transform of all obtained mode components to obtain a instantaneous frequency; according to the time-frequency property of the ocular artifacts in the EEG signal and the statistical property of the empirical mode components, performing the threshold filtering of all obtained mode components; and performing data reconstruction by using all mode components obtained after filtration. The method solves the manual screening problem of the empirical mode components having the ocular artifacts, thereby automatically removing the ocular artifacts from the EEG signal.

Description

Technical field: [0001] The present invention relates to the field of biological information technology, in particular to electroencephalography (EEG) acquisition and preprocessing technology. Specifically, it relates to the automatic removal technology of electrooculography (EOG) based on Hilbert-Huang Transform (HHT) EEG signals. Background technique: [0002] The analysis and research of EEG signals play a very important role in neuroscience, psychology, biomedicine and other fields. However, the EEG signal is a very weak electrophysiological signal, which is easily affected by various types of interference sources during the acquisition process. EEG interference has caused great difficulties in analyzing EEG signals, especially hindering the automatic computer analysis and diagnosis of EEG signals. Therefore, how to effectively detect and eliminate various interference components in EEG to extract true and reliable EEG information plays a vital role in the analysis and rese...

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): A61B5/0476G06F19/00
Inventor 李明爱杨林豹林琳杨金福阮晓钢左国玉
Owner BEIJING UNIV OF TECH
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