Electroencephalogram feature classification method based on PSO-SVM (Particle Swarm Optimization-Support Vector Machine)

Abstract

The invention relates to an electroencephalogram feature classification method based on a PSO-SVM (Particle Swarm Optimization-Support Vector Machine). The electroencephalogram feature classification method comprises the following steps: firstly, utilizing a regularizable CSP (R-CSP) (Cryptographic Service Provider) algorithm to carry out electroencephalogram feature extraction; secondly, utilizing the particle swarm optimization to optimize the penalty factor C and the nuclear parameter g of the support vector machine; and finally, training a SVM classifier by an obtained optimal parameter, and utilizing the trained classifier to carry out classification prediction on a sample. Compared with traditional SVM classification identification, a result indicates that the classification identification algorithm based on the PSO-SVM can effectively improve the classification identification rate of the electroencephalogram. Compared with the traditional classification identification method, the electroencephalogram feature classification method has obvious advantages.

Description

technical field [0001] The invention relates to a method for extracting and classifying features of electroencephalogram signals, in particular to a method for classifying features of electroencephalogram signals based on PSO-SVM. Background technique [0002] Brain-Interface (BCI) is a communication and control interface between the human brain and computers or other devices based on electroencephalogram (EEG). BCI technology refers to a new type of human-computer interaction method that enables people to output control signals through computers and other electronic devices without the participation of peripheral nervous system and muscle tissue, and then communicate with the external environment. In recent years, brain-computer interface technology based on EEG has become a research hotspot in the field, and has gradually developed into a new multidisciplinary technology. [0003] The key technology of BCI is how to quickly and effectively extract EEG signal features and ...

AI Technical Summary

Problems solved by technology

However, due to the randomness and non-stationarity of the EEG signal during the use of SVM, and the lack of prior knowledge of the distribution characteristics of the signal by researchers, there is uncertainty in the selection of the optimal kernel function of SVM.

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