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Emotional detection method based on brain electric wave analysis

A detection method and brain wave technology, applied in diagnostic recording/measurement, medical science, diagnosis, etc., can solve the problems of complex operation process and low detection accuracy, and achieve the effect of simple operation and high classification accuracy

Inactive Publication Date: 2017-02-22
CHONGQING UNIV
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AI Technical Summary

Problems solved by technology

The above method needs to retrain the classification model when testing each target, the operation process is complicated, and the detection accuracy is low

Method used

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  • Emotional detection method based on brain electric wave analysis
  • Emotional detection method based on brain electric wave analysis
  • Emotional detection method based on brain electric wave analysis

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Embodiment Construction

[0037] The specific implementation manner and working principle of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0038] Such as figure 1 As shown, an emotion detection method based on brain wave analysis, the specific steps are as follows:

[0039] Step 1: Perform feature extraction on the acquired brain wave sample signal, and extract fractal dimension features, energy features, statistical features and high-order intersection features respectively. The specific operations are as follows:

[0040] In this example, the fractal dimension feature is obtained after processing the brain wave signal by the Higuchi algorithm,

[0041] First, the acquired brain wave sample signal is subjected to interval sampling processing according to the following formula, and the processing formula is To obtain a new time series sample For each new time series sample Calculate its deviation mean L n (k), the calculation ...

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Abstract

The invention discloses an emotion detection method based on brain electric wave analysis. The method includes the following steps: firstly, extracting features by using the original electroencephalo-graph data, wherein the features include fractal dimension feature, energy feature, statistical feature and high-order cross feature; evaluating the feature parameters of the extracted features by an intraclass correlation coefficient-like method so as to obtain the most stable feature parameter; allowing a support vector machine to use the obtained feature parameters to train a classification model; finally, conducting the real-time detection of the emotion with allowing the trained classification model. The emotion detection method has the remarkable advantages that the most stable feature parameter can be obtained by the intraclass correlation coefficient-like method, and the stable and accurate classification model is thus successfully trained; compared with the traditional method, there is no need to re-train the classification model, the operation is simpler and the classification accuracy is higher.

Description

technical field [0001] The invention relates to the technical field of digital signal processing, in particular, an emotion detection method based on brain wave analysis. Background technique [0002] Brain waves are formed by the sum of the synchronous post-synaptic potentials of a large number of neurons when the brain is active. It records the electric wave changes during brain activity, which is the overall reflection of the electrophysiological activities of brain nerve cells on the surface of the cerebral cortex or scalp. With the advancement of science and technology, the application of brain waves has also begun to develop from the medical field to the engineering application field. Currently, emotion detection methods based on brain wave analysis are receiving high attention. [0003] In recent years, in the process of studying emotion detection methods based on brainwave analysis, researchers have applied different brainwave features and corresponding classifiers...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/0476A61B5/16
CPCA61B5/165A61B5/725A61B5/7257A61B5/7264A61B5/7267A61B5/7271A61B5/316A61B5/369
Inventor 李正浩许典李鸿鹄陈凯龚卫国李伟红杨利平
Owner CHONGQING UNIV
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