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Electroencephalogram signal analysis method based on complex network and application

A technology of EEG signal and analysis method, applied in the fields of application, medical science, sensor, etc., to achieve the effect of high time-frequency resolution

Pending Publication Date: 2020-11-27
EASTERN GANSU UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the above-mentioned defects of the prior art, the embodiment of the present invention provides a complex network-based EEG signal analysis method and its application, which resolves the signal data with obvious local characteristics in the time domain and frequency domain but is difficult to process Analysis, the rapid transient phenomena shown in the data can be more accurately and effectively identified, and the multi-scale analysis can be used to quickly and effectively extract the originally complicated EEG signal network, and extract the EEG signals needed by researchers to deal with the inability to Knowing exactly when a stimulus occurs requires capturing real-time EEG signals

Method used

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Embodiment 1

[0021] The present invention provides a complex network-based EEG signal analysis method and application, specifically comprising the following steps:

[0022] Step 1: Collect signals, randomly select 30 suitable research subjects, place these 30 research subjects in the same environment, collect the digital EEG signals of the subjects in a state of quietness, sobriety and eyes closed, and control the sampling frequency. Obtain a piece of data in a time period, and use this data as a data module, where the sampling frequency is set to 200Hz, and the time period contained in the data module is set to 15 seconds;

[0023] Step 2: Build an EEG signal network, store several sets of data collected in Step 1 into the computer, and perform preprocessing. The specific operation of preprocessing is to first band-pass filter the collected EEG data to remove the EEG The high and low frequency interference components in the data, and then manually remove the artifact data from the EEG dat...

Embodiment 2

[0029] The present invention provides a complex network-based EEG signal analysis method and application, specifically comprising the following steps:

[0030] Step 1: Collect signals, randomly select 40 suitable research subjects, place these 40 research subjects in the same environment, collect the digital EEG signals of the subjects in a quiet, awake state with their eyes closed, and control the sampling frequency. Obtain a piece of data in a time period, and use this data as a data module, where the sampling frequency is set to 220Hz, and the time period included in the data module is set to 17 seconds;

[0031] Step 2: Build an EEG signal network, store several sets of data collected in Step 1 into the computer, and perform preprocessing. The specific operation of preprocessing is to first band-pass filter the collected EEG data to remove the EEG The high and low frequency interference components in the data, and then manually remove the artifact data from the EEG data to...

Embodiment 3

[0037] The present invention provides a complex network-based EEG signal analysis method and application, specifically comprising the following steps:

[0038] Step 1: Collect signals, randomly select 50 suitable research subjects, place these 50 research subjects in the same environment, collect the digital EEG signals of the subjects in a quiet, awake state with their eyes closed, and control the sampling frequency. Obtain a piece of data in a time period, and use this data as a data module, where the sampling frequency is set to 240Hz, and the time period contained in the data module is set to 20 seconds;

[0039] Step 2: Build an EEG signal network, store several sets of data collected in Step 1 into the computer, and perform preprocessing. The specific operation of preprocessing is to first band-pass filter the collected EEG data to remove the EEG The high and low frequency interference components in the data, and then manually remove the artifact data from the EEG data t...

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PUM

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Abstract

The invention discloses an electroencephalogram signal analysis method based on a complex network and an application, and particularly relates to the technical field of biological signal processing. The method specifically comprises the following steps of 1, collecting signals; 2, constructing an electroencephalogram signal network; 3, performing quantitative analysis; 4, performing matching and tracking; 5, carrying out wavelet transformation; and step 6, carrying out data analysis. According to the method, resolution analysis is carried out on signal data of which local features are obviousbut relatively difficult to process in a time domain and a frequency domain, a rapid instantaneous phenomenon displayed by the data can be identified more accurately and effectively, the original complex electroencephalogram network can be extracted rapidly and effectively by using multi-scale analysis, and the electroencephalogram signals needed by researchers are extracted to cope with the phenomenon that the stimulation occurrence time cannot be known accurately but the real-time electroencephalogram signals need to be captured.

Description

technical field [0001] The present invention relates to the technical field of biological signal processing, and more specifically, the present invention relates to a complex network-based EEG signal analysis method and its application. Background technique [0002] Electroencephalogram (EEG) is the overall reflection of the electrophysiological activity of brain nerve cells on the surface of the cerebral cortex or scalp. EEG signals contain a large amount of physiological and disease information. In clinical medicine, EEG signal processing can not only provide diagnosis basis for some brain diseases, but also provide effective treatment methods for some brain diseases. In terms of engineering applications, people are also trying to use EEG signals to realize the Brain-Computer Interface (BCI), using the differences in EEG for different sensory, motor or cognitive activities, and effectively extracting and classifying EEG signals. achieve some kind of control purpose. [0...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/0476
CPCA61B5/726A61B5/7235A61B5/7203A61B5/7225
Inventor 任亚莉仵博万
Owner EASTERN GANSU UNIVERSITY
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