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A kind of recognition method of electroencephalogram signal

An electroencephalographic signal and identification method technology, applied in the field of electroencephalographic signal identification, can solve the problems of hindering the popularization and application of support vector machines of P300 signals, poor overall generalization ability, and low classification accuracy, so as to optimize the identification performance, improve the The effect of identifying performance and improving intelligence

Active Publication Date: 2020-06-05
DONGHUA UNIV
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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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  • A kind of recognition method of electroencephalogram signal
  • A kind of recognition method of electroencephalogram signal
  • A kind of recognition method of electroencephalogram signal

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Embodiment Construction

[0045] The present invention will be further described below in combination with specific embodiments. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present application.

[0046] A method for identifying EEG signals, such as figure 1 As shown, the steps are as follows:

[0047] (1) Carry out character experiments and extract EEG signals containing P300 signals as a training set. The specific operation is: select a character on the 6×6P300 character speller, and then the character speller flashes randomly row by row or column by column to generate EEG Signal, when the row or column where ...

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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 electroencephalogram signal identification, and relates to an identification method of electroencephalogram signals, in particular to an identification method of electroencephalogram signals based on an immune algorithm-based integrated support vector machine. Background technique [0002] 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 an obvious upward trend. In particular, the holding of two BCI international conferences in 1999 and 2002 pointed out the direction. At present, BCI technology has attracted the general attention of scientific and technological workers in many disciplin...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/00A61B5/0476
CPCA61B5/7264A61B5/369
Inventor 任立红李嘉伟丁永生郝矿荣陈磊
Owner DONGHUA UNIV
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