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Electroencephalogram multi-domain feature extraction method based on multivariate variational mode decomposition

A technique of variational modal decomposition and feature extraction, which is applied in the field of Ming Dynasty and can solve the problems of ignoring time domain features and spatial distribution components.

Active Publication Date: 2020-09-04
HANGZHOU DIANZI UNIV
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Problems solved by technology

The above methods all show good adaptability and high recognition accuracy, but they all ignore the important time-domain features and spatial distribution components in each type of motor imagery EEG signal

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  • Electroencephalogram multi-domain feature extraction method based on multivariate variational mode decomposition
  • Electroencephalogram multi-domain feature extraction method based on multivariate variational mode decomposition
  • Electroencephalogram multi-domain feature extraction method based on multivariate variational mode decomposition

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Embodiment Construction

[0022] The MVMD-based EEG multi-domain feature extraction of the present invention will be described in detail below in conjunction with the accompanying drawings, as figure 1 , the implementation of the present invention mainly comprises 6 steps: (1) collect multi-channel EEG signal and preprocessing, (2) carry out MVMD decomposition to multi-channel EEG signal, obtain some intrinsic mode function IMF components, (3) According to the Hilbert spectrum analysis of each IMF component, the instantaneous energy mean value feature of the EEG signal is obtained, (4) extract the multi-scale sample entropy feature for each IMF component, (5) extract the variance vector from the new signal matrix composed of components through the CSP algorithm , (6) Combining the features obtained in (3)(4)(5) into the classifier for classification to obtain the result.

[0023] Each step will be described in detail below one by one.

[0024] Step (1): In this example, the BCI Competition II Dataset ...

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Abstract

The invention discloses an electroencephalogram multi-domain feature extraction method based on multivariate variational mode decomposition. The method comprises the following steps: firstly, carryingout adaptive decomposition on original electroencephalogram multi-channel data by using multivariate variational mode decomposition (MVMD), and then extracting time domain features and nonlinear dynamics features of signals from intrinsic mode function (IMF) components obtained by decomposition; meanwhile, combining the IMF components to construct a new signal matrix, extracting spatial featuresof the reconstructed signals by adopting a common spatial pattern (CSP) method, and combining the time domain, nonlinear dynamics and spatial domain features; and finally classifying the feature set through a support vector machine (SVM). According to the method, important information components related to a specific task can be effectively distinguished, and a new idea is provided for feature extraction of the electroencephalogram signals.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and is aimed at motor imagery electroencephalogram (EEG), and proposes to use multivariate variational mode decomposition (MVMD) to carry out adaptive decomposition on the original EEG multi-channel data, and then obtain the intrinsic mode function from the decomposition The (IMF) component extracts the time-domain features and nonlinear dynamic features of the signal. At the same time, the IMF components are combined to construct a new signal matrix, and the common space pattern (CSP) method is used to extract the spatial features of the reconstructed signal to perform time-domain, Combination of nonlinear dynamics and spatial features features and classification methods. Background technique [0002] Brain-computer interface technology (BCI) is a human-computer interaction system that directly communicates information between the human brain and the outside world without relying on the normal...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/0476
CPCA61B5/7225
Inventor 孟明闫冉尹旭戴橹洋胡家豪
Owner HANGZHOU DIANZI UNIV
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