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Method for removing BCG artifacts through synchronous EEG-fMRI electroencephalogram signals

A technology of EEG signals and electrical signals, applied in the field of BCG artifacts, can solve problems such as inability to obtain EEG signals, achieve the effects of retaining EEG information, reducing equipment requirements, and avoiding processing errors

Active Publication Date: 2021-07-09
HANGZHOU DIANZI UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, there are few studies on this kind of unpaired EEG signal conversion problem (that is, the contaminated EEG signal and the pure EEG signal at the same time cannot be obtained for supervised signal conversion in practice).

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  • Method for removing BCG artifacts through synchronous EEG-fMRI electroencephalogram signals
  • Method for removing BCG artifacts through synchronous EEG-fMRI electroencephalogram signals
  • Method for removing BCG artifacts through synchronous EEG-fMRI electroencephalogram signals

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Embodiment Construction

[0070] The method for removing BCG artifacts in EEG signals collected by synchronous EEG-fMRI based on deep learning of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0071] Such as figure 1 As shown, a method based on deep learning to remove BCG artifacts in EEG signals acquired by synchronous EEG-fMRI includes the following steps:

[0072] Step 1. EEG data collection: Under the supervision of experienced clinicians and with the consent of the subjects, the EEG data of eyes open and eyes closed in normal environment and MRI environment were collected respectively. Signal acquisition A 32-channel MRI-compatible BP product was used to record EEG data, the impedance was adjusted to below 10kΩ, and the sampling rate was 5000Hz. According to the standard 10-20 system, 30 EEG electrodes (Fp1, Fp2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T7, T8, P7, P8, Fz, Cz, Pz , Oz, FC1, FC2, CP1, CP2, FC5, FC6, CP5, CP6, A1, A2) placed...

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Abstract

The invention discloses a method for removing BCG artifacts through synchronous EEG-fMRI electroencephalogram signals. The method comprises the following steps: collecting and preprocessing multi-channel scalp electroencephalogram signals in eye opening and closing states in a common environment and a nuclear magnetic resonance environment, then performing data segmentation on the multi-channel scalp electroencephalogram signals, and constructing an eye opening and closing state data set; respectively training an eye-opening network model and an eye-closing network model for removing BCG artifacts by utilizing the eye-opening and eye-closing state data sets; according to the model, a network architecture model BCGGAN based on a CycleGAN is adopted; and the BCGGAN comprises a CycleGAN, an auto-encoder constraint and an intermediate feature constraint. According to the method, while the BCG artifacts are removed as far as possible, electroencephalogram information can be better and effectively reserved.

Description

technical field [0001] The invention relates to the field of EEG signal preprocessing, in particular to a deep learning-based method for removing BCG artifacts in EEG signals collected by synchronous EEG-fMRI. Background technique [0002] With the development of brain science research, the combination of electroencephalogram signal (EEG) and functional magnetic resonance imaging (fMRI) has attracted extensive attention and research. EEG signals directly measure the electrical activity of brain nerves, with a temporal resolution of milliseconds; while functional magnetic resonance imaging indirectly measures brain activity by measuring changes in blood oxygen levels in the brain, with spatial resolution of millimeters. The complementarity of EEG and fMRI in spatial and temporal resolution enables simultaneous EEG-fMRI to provide more in-depth and comprehensive information for brain function research. [0003] The first thing to be faced with in synchronous EEG-fMRI research...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/369A61B5/372A61B5/00G06N3/04G06N3/08
CPCA61B5/7235A61B5/7203G06N3/084G06N3/045G06N3/044
Inventor 林广任彬刘予晞张建海
Owner HANGZHOU DIANZI UNIV
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