Brain wave analysis method based on Hilbert-Huang transform and support vector machine optimization

A technology of support vector machine and analysis method, applied in the field of computer signal processing, can solve the problem that the parameters are not optimal, and achieve the effect of reducing generalization error, improving robustness and strong adaptability
CN112668402APending Publication Date: 2021-04-16SHANDONG UNIV
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Patent Information

Authority / Receiving Office
CN Β· China
Current Assignee / Owner
SHANDONG UNIV
Publication Date
2021-04-16

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Abstract

The invention relates to the field of computer signal processing, in particular to a brain wave signal analysis method. The invention discloses a brain wave analysis method based on Hilbert-Huang transform and optimization of an artificial bee colony algorithm and optimization of a support vector machine. The method comprises the following steps: collecting brain wave signal data; wherein the brain wave data are brain wave signals corresponding to imagination of different motion states; decomposing the original brain wave signal by adopting an empirical mode decomposition method to obtain a series of intrinsic mode functions; extracting brain wave features from the intrinsic mode function; and taking the extracted brain wave features as input vectors, and classifying the input vectors by using a classifier so as to distinguish motion states corresponding to the brain wave signals. According to the method, Hilbert-Huang transform is used to extract features, and artificial bee colony algorithm is used to optimize support vector machine classification, so that the method has stronger adaptability, better classification capability and higher calculation efficiency, and is helpful to improve the accuracy of brain wave classification.
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Description

technical field

[0001] The invention relates to the field of computer signal processing, and relates to a method for analyzing brain wave signals. Background technique

[0002] After the signal undergoes Fourier transform, the information in the time domain is completely lost, while the Hilbert-Huang transform reflects the local two-dimensional information in the time domain and frequency domain of the signal in a more detailed manner. The Hilbert-Huang transform is a method that can be used for time-frequency analysis of non-stationary signals, which was proposed by Norden E. Huang (Huang E) et al. in 1998. The Hilbert-Huang transform obtains the intrinsic mode function through the empirical mode decomposition of non-stationary and nonlinear signals, which makes the instantaneous frequency and amplitude have physical meaning. And this transformation method does not set the basis function in advance, the transformation method itself can automatically select the basis functi...

Claims

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