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Electroencephalogram feature extraction method based on non-Gaussian time sequence model

A feature extraction and time series model technology, applied in medical science, sensors, diagnostic recording/measurement, etc., can solve problems such as poor effect, achieve good noise resistance, high sensitivity, and remove artifacts

Active Publication Date: 2015-04-29
浙江浙大西投脑机智能科技有限公司
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  • Application Information

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Problems solved by technology

However, this invention is specific to the characteristics of EEG data, and the effect is poor when the characteristic values ​​are inconsistent

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  • Electroencephalogram feature extraction method based on non-Gaussian time sequence model
  • Electroencephalogram feature extraction method based on non-Gaussian time sequence model
  • Electroencephalogram feature extraction method based on non-Gaussian time sequence model

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Embodiment Construction

[0075] The method for extracting EEG features based on the non-Gaussian time series model of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0076] A method for extracting EEG features based on a non-Gaussian time series model, comprising the following steps:

[0077] (1) Obtain the EEG data to be processed and two sets of training EEG data, remove the artifacts in the EEG data to be processed and the two sets of training EEG data, and obtain the effective frequency band of the EEG data to be processed and two sets of training EEG data respectively. Then divide the effective frequency band of EEG data to be processed and the effective frequency band of each group of training EEG data into several data segments; each group of training EEG data includes EEG data in two brain states. data.

[0078] Each set of training EEG data contains EEG signal data in two brain states, and the signal of each brain state is continuous a...

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Abstract

The invention discloses an electroencephalogram feature extraction method based on a non-Gaussian time sequence model. The method comprises the following steps: acquiring electroencephalogram data to be processed and two groups of training electroencephalogram data, removing artifacts and partitioning an obtained effective frequency band into a plurality of data segments; extracting the time-frequency feature value, morphological feature value and complexity feature value of each data segment, wherein the feature value of each data segment constructs a feature vector; marking the status value of each feature vector in a first group of training electroencephalogram data, and training a support vector machine by using a marking result; inputting the feature vectors of a second group of training electroencephalogram data into the support vector machine to obtain the status value sequence of the second group of training electroencephalogram data; establishing an observation equation and a status transfer equation, and determining parameters in the equations by using the feature vectors and the status value sequence of the second group of electroencephalogram training data; acquiring the status value of the electroencephalogram data to be processed by using the feature vector of the electroencephalogram data to be processed and the two equations. By adopting the method, different brain statuses can be distinguished accurately.

Description

technical field [0001] The invention relates to the field of EEG data analysis, in particular to an EEG feature extraction method based on a non-Gaussian time series model. Background technique [0002] Scalp EEG signal data contains a large amount of information related to brain characteristics and states, and is an important tool for judging brain states. Due to the time-varying and nonlinear nature of the brain, the signals it generates are also time-varying and nonlinear. At the same time, because the scalp EEG signals are easily interfered by eye electricity, myoelectricity, and ECG signals, effective and robust EEG Signal feature extraction methods become a difficult problem. [0003] The invention with the application number of 200910196746.3 discloses a brain wave analysis method, which uses the classic time-frequency domain analysis and principal component analysis methods to solve the problem of brain wave feature extraction, and successfully extracts the features...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/0476
Inventor 王跃明祁玉郑筱祥张建明朱君明
Owner 浙江浙大西投脑机智能科技有限公司
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