EEG signal feature recognition system and method

A technology of EEG signal and feature recognition, applied in medical science, diagnosis, psychological devices, etc., can solve the problems of increasing algorithm complexity, interference of emotion recognition, and reducing the accuracy of emotion recognition

Active Publication Date: 2020-06-09
上海零唯一思科技有限公司
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AI Technical Summary

Problems solved by technology

Although this method can collect relatively stable EEG signals, its disadvantage is that the subject needs to apply conductive paste for each collection. The workload of this process is huge, and the preparation process is also very cumbersome. Prolonged exposure can cause discomfort
In addition, the EEG signals in all electrodes may have redundant information that has nothing to do with emotions. If all of them are used, it will not only increase the complexity of the algorithm, but also interfere with emotion recognition and reduce the accuracy of emotion recognition.

Method used

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  • EEG signal feature recognition system and method
  • EEG signal feature recognition system and method

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Embodiment Construction

[0050] like figure 2 As shown, this embodiment includes the following steps:

[0051] Step 1: 6 subjects aged between 20 and 24 who are physically and mentally healthy and without mental illness watch movie clips with four emotional categories, and induce their four emotions of happiness, sadness, fear and calm. Each emotion There are 6 movie clips, for a total of 24 movie clips. While watching, according to figure 1 The indicated electrode positions collect the EEG signals of the subjects. Each subject did three experiments, a total of 18 sets of experimental data.

[0052] Step 2: Pass the EEG data collected in each experiment through a band-pass filter in the range of 1-75 Hz to filter out noise and artifacts.

[0053] Step 3: Perform short-time Fourier transform on the preprocessed EEG signal to obtain energy features in five frequency bands, and then extract differential entropy features.

[0054] Step 4, use the linear dynamical system method to perform feature smo...

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Abstract

Provided are a feature recognition system and method for electroencephalogram signals. The method comprises the following steps: utilizing four conduction electrodes of a temporal area above ears to collect original brain electrical signals in different emotion states of different people and forming a sample set; obtaining electroencephalogram feature data from the sample set by pre-processing and extracting features; carry out smoothing operation on electroencephalogram feature data to obtain a training sample used for training a support vector machine so that an emotion recognition classifier is obtained. The feature recognition system and method for electroencephalogram signals have the following beneficial effects: under the premise that acquisition cost and complexity of electroencephalogram are greatly reduced, the feature recognition system and method can maintain higher emotion recognition accuracy and provide useful reference on emotion recognition by wearable equipment.

Description

technical field [0001] The present invention relates to a technology in the field of EEG signal detection, in particular to an EEG signal feature recognition system and method based on four conductive electrodes above the ear. Background technique [0002] Emotions can reflect a person's cognition and attitude, can affect people's psychology and behavior, and are an important part of people's daily life. With the rapid development of human-computer interaction applications, people hope to have more humanized computers to assist people in completing work tasks, which requires computers to have certain emotion recognition capabilities. In the process of human-computer interaction, if the computer can quickly and accurately identify the emotional state of the person, it can adjust its work content and methods according to the emotional state of the person, improve the experience of human-computer interaction, and make the process of human-computer interaction more efficient. F...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/0476A61B5/16A61B5/00
CPCA61B5/165A61B5/725A61B5/7257A61B5/7264A61B5/7267A61B5/7271A61B5/316A61B5/369
Inventor 吕宝粮郑伟龙陆怡菲
Owner 上海零唯一思科技有限公司
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