EEG channel selecting method based on standard mutual information

A channel selection and mutual information technology, applied in the field of emotion recognition, can solve problems such as incomplete channel selection results, achieve the effects of improving classification accuracy, reducing the number of channels, and solving design complexity problems

Active Publication Date: 2019-10-22
XIAN UNIV OF POSTS & TELECOMM
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to propose an EEG channel selection method based on standard mutual information, so as to overcome the problem in the prior art that the channel selection result will be affected and the channel selection result will be incomplete

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  • EEG channel selecting method based on standard mutual information
  • EEG channel selecting method based on standard mutual information
  • EEG channel selecting method based on standard mutual information

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Embodiment Construction

[0029] The present invention will be further described in detail below through specific embodiments in conjunction with the accompanying drawings. Wherein, similar elements in different implementations adopt associated similar element numbers. In the following implementation manners, many details are described for better understanding of the present application. However, those skilled in the art can readily recognize that some of the features can be omitted in different situations, or can be replaced by other elements, materials, and methods.

[0030] The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0031] Example,

[0032] see figure 1 and figure 2 , an EEG channel selection method based on standard mutual information, including word segmentation to obtain a time-frequency map, building an association matrix, and using a threshold to select channels. The specific steps are as follows:

[0033] Step ...

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Abstract

The invention relates to the emotion recognition field and the EEG channel selection technology, in particular to an EEG channel selecting method based on standard mutual information. The method includes the following steps of collecting data, extracting EEG through a public data set, conducting the short-time Fourier transform on the EEG to obtain a time-frequency graph, establishing an incidencematrix by calculating standard mutual information values between all channels in the time-frequency graph, analyzing the incidence matrix, setting the threshold selection channel, determining the optimum channel, and classifying data through a support vector machine classifier after the channel is selected to obtain the emotion recognition rate. The method has the advantages that the high emotionrecognition rate is still kept under the condition of greatly reducing the number of the channels, the selected channel provides an applicable scheme for the design of a wearable EEG device, the design complexity problem of hardware is solved, the classification accuracy of EEG can be effectively improved, and the data redundancy and computation complexity caused by using signals of all the channels are overcome.

Description

technical field [0001] The invention relates to the field of emotion recognition and an EEG signal channel optimization technology, specifically a method for selecting an EEG channel based on standard mutual information. Background technique [0002] As a high-level function of brain activity, emotion affects people's work and life to a large extent. With the rise of artificial intelligence technology, emotion recognition has also received the attention of scholars. At present, emotion recognition mainly revolves around two aspects: people's external behavior and objective physiological signals. Because physiological signals are spontaneous and not controlled by subjective consciousness, they can more objectively reflect people's true emotions. In actual research, more physiological signals such as EEG, EMG, and skin electricity are used. Among them, the EEG signal is directly generated by the central nervous system of the human being, which more directly and objectively ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/0476A61B5/16A61B5/00
CPCA61B5/165A61B5/7235A61B5/7257A61B5/7267A61B5/316A61B5/369
Inventor 宋辉胡书源衡霞张荣贺炎
Owner XIAN UNIV OF POSTS & TELECOMM
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