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Improved SVM electroencephalogram signal emotion recognition method

A technology of emotion recognition and EEG signal, applied in the field of signal processing, can solve problems such as the randomness of model classification performance

Inactive Publication Date: 2021-01-26
XI'AN POLYTECHNIC UNIVERSITY
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Problems solved by technology

Since the parameters c and g of the traditional SVM model need to be selected based on the experience of the modeler, the classification performance of the model has a large randomness.

Method used

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  • Improved SVM electroencephalogram signal emotion recognition method
  • Improved SVM electroencephalogram signal emotion recognition method
  • Improved SVM electroencephalogram signal emotion recognition method

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Embodiment Construction

[0052] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0053] Such as figure 1 As shown, an improved SVM EEG signal emotion recognition method is specifically implemented according to the following steps:

[0054] Step 1, preprocessing the DEAP database;

[0055] Step 2, extracting emotional features;

[0056] Step 3. Classify the extracted emotion feature data with a PSO-SVM classifier.

[0057] The preprocessing of the DEAP database in step 1 includes: sampling the experimental data and removing noise, and the sampling frequency is 128Hz.

[0058] The database in step 1 included 32 test subjects, including 16 males and 16 females; 32 sensors were used in the frontal lobe, parietal lobe, occipital lobe, and temporal lobe of the brain respectively, and the experimenters looked at 40 subjects respectively. The signal of a segment of video, each video time is 60s; for each subject, there are tw...

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Abstract

The invention discloses an improved SVM electroencephalogram signal emotion recognition method. The method is specifically implemented according to the following steps of 1, pre-processing a DEAP database; 2, extracting emotion features; and 3, classifying the extracted emotion feature data by using a PSO-SVM classifier. Experimental results show that the average accuracy of emotion dichotomy of titer and awakening degree by adopting a PSO-SVM algorithm is 60.53% and 65.66% respectively, the accuracy is improved by 5.05% and 1.85% respectively compared with a traditional SVM algorithm, the accuracy is superior to that of the traditional SVM algorithm, and the better recognition accuracy of the PSO-SVM algorithm is effectively verified.

Description

technical field [0001] The invention belongs to the technical field of signal processing and relates to an improved SVM electroencephalogram signal emotion recognition method. Background technique [0002] Emotion recognition based on EEG signals is to identify people's emotional state by obtaining people's physiological and non-physiological signals. For electrical signals, preprocess the collected EEG signals and perform feature extraction, and input the processed data into the SVM classification model for inspection. Since the parameters c and g of the traditional SVM model need to be selected based on the experience of the modeler, the classification performance of the model has a large randomness. Contents of the invention [0003] The purpose of the present invention is to provide an improved SVM EEG signal emotion recognition method, which has the characteristics of obtaining the optimal parameters c and gamma of the SVM model, thereby improving the accuracy of rec...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/00G06F17/14
CPCG06N3/006G06F17/148G06F2218/08G06F2218/12G06F18/2411G06F18/214
Inventor 张晓丹杜金祥翟雅文刘东晓李涛朱磊崔琳赵瑞
Owner XI'AN POLYTECHNIC UNIVERSITY
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