Electroencephalogram (EEG) recognition method

Abstract

The invention relates to an electroencephalogram (EEG) recognition method. The method comprises the following steps: carrying out a character experiment to extract an EEG including 300 signals so as to serve as a training set; integrating a plurality of support vector machines with mixed kernels to serve as a learner by utilizing a bagging algorithm, and adaptively adjusting parameters of the learner based on an immune algorithm by adopting the training set so as to obtain optimum parameters; and finally, recognizing a P300 signal in the EEG by utilizing the learner with the optimum parameters, wherein the optimum parameters refer to parameters capable of enabling the learner to accurately recognize the P300 signal, and accurate recognition is that the accuracy of repeated experiments of more than 12 times is 96-98%. According to the EEG recognition method disclosed by the invention, the parameters can be intelligently selected according to optimized contents, the defects that the traditional learner needs to be continuously adjusted and optimized and needs a cross validation process are overcome, the intelligence of the integrated learner is improved, and the EEG recognition method disclosed by the invention is excellent in recognition performance, high in accuracy rate and high in overall generalization ability and has excellent popularization and application values.

Description

Technical field [0001] The invention belongs to the field of brain electrical signal identification, and relates to a brain electrical signal identification method, in particular to a brain electrical signal identification method based on an immune algorithm integrated support vector machine. Background technique [0002] The Brain-Computer Interface (BCI) technology was formed in the 1970s and is a cross-cutting technology involving neuroscience, signal detection, signal processing, pattern recognition and other fields. For more than 20 years, with the improvement of people’s understanding of nervous system functions and the development of computer technology, the research on BCI technology has shown a clear upward trend, especially the two BCI international conferences held in 1999 and 2002 indicate the development of BCI technology Up the direction. At present, BCI technology has attracted the general attention of scientific and technological workers in many disciplines in th...

AI Technical Summary

Problems solved by technology

Since the P300 classification is a binary classification problem, the support vector machine classification algorithm suitable for binary classification is widely used. Many scholars use support vector machines to classify EEG signals and have achieved good results. However, single support vector machines generally have classification problems. Problems such as limited ability, low classification accuracy, and poor overall generalization ability hinder the further promotion and application of support vector machines that can identify P300 signals

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