EEG (electroencephalogram) feature extraction method

A feature extraction and EEG technology, applied in the field of EEG data analysis, can solve problems such as poor results

Active Publication Date: 2012-12-19
浙江浙大西投脑机智能科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

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

Method used

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  • EEG (electroencephalogram) feature extraction method
  • EEG (electroencephalogram) feature extraction method
  • EEG (electroencephalogram) feature extraction method

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Embodiment Construction

[0058] Below in conjunction with accompanying drawing, a kind of EEG feature extraction method of the present invention is described in detail:

[0059] A method for extracting EEG features, comprising the following steps:

[0060] (1) Use band-pass filtering to remove EEG data artifacts to obtain effective frequency bands, and use sliding time windows to evenly divide the effective frequency bands into several data segments;

[0061] First, preprocess the EEG data, remove artifacts from the original EEG data through band-pass filtering, and select an effective frequency band for analysis and processing. The band-pass filtering uses a second-order Butterworth filter (Butterworth), and the filtering parameters are 1.6~70Hz, that is, the obtained effective frequency band is 1.6~70Hz.

[0062] The obtained effective frequency band is evenly divided into several data segments, the smallest unit of data processing is determined, and the obtained effective frequency band is decompo...

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Abstract

The invention discloses an EEG (electroencephalogram) feature extraction method. The EEG feature extraction method includes the steps of removing artifacts in background EEG data and EEG data to be processed to obtain effective frequencies of the background EEG data and the EEG data to be processed, and respectively dividing the effective frequencies of the background EEG data and the EEG data to be processed into a plurality of data segments; extracting time-frequency feature and morphological feature of each data segment to obtain time-frequency feature value and morphological feature value of each data segment; calculating to obtain a frequency distribution function of the feature values according to the time-frequency feature value and the morphological feature value of each data segment of the background EEG data; obtaining probabilities of occurrence of the time-frequency feature value and the morphological feature value of each data segment of the EEG data to be processed according to the frequency distribution functions of the background EEG data; and calculating to obtain IMF-VoE (intrinsic mode functions and upper and lower envelopes) feature values according to the probabilities of the feature values. Change features of EEG signals can be quickly and effectively recognized by the IMF-VoE feature values to monitor brain statuses.

Description

technical field [0001] The invention relates to the field of EEG data analysis, in particular to an EEG feature extraction method. Background technique [0002] The EEG data signal is an information carrier that carries the characteristics or state of the human brain. The human brain is an open, time-varying and nonlinear system, and the signals it generates are also time-varying and nonlinear. At the same time, the EEG data signal is in the Random errors will occur after measurement, and EEG signals will be hardened by individual differences. Therefore, the analysis of EEG data signals has 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 closely related to human tension, fatigue and relaxation. The ti...

Claims

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Application Information

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