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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

Pending Publication Date: 2021-04-16
SHANDONG UNIV
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Problems solved by technology

There are many parameters in the support vector machine kernel function, however, the parameters selected according to experience are often not optimal

Method used

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

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Embodiment Construction

[0025] In order to facilitate the understanding of the present invention, the present invention will be described in more detail below in conjunction with the accompanying drawings and specific embodiments. Preferred embodiments of the invention are shown in the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described in this specification. On the contrary, these embodiments are provided to make the understanding of the present disclosure more thorough and comprehensive.

[0026] One of the embodiments provided by the present invention is: a brain wave analysis method based on Hilbert-Huang transform and support vector machine optimization, the process is as follows figure 1 As shown, it specifically includes the following steps:

[0027] S1, collecting EEG data. Dataset 2a records the EEG signal data of 10 subjects and 22 electrodes. Each subject has a total of 288 experiments, and the s...

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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.

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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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/00
Inventor 冯德军何昕朱佳成刘洋
Owner SHANDONG UNIV
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