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Method of Removing Noise from EEG Signals Based on HHT Method

机译:基于HHT方法的脑电信号噪声去除方法

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Many noises are interfused into EEG signals when they are measuring. In order to remove the noises effectively, a novel method based on Hilbert Huang Transform, is shown in the thesis. The theories of empirical mode decomposition and instantaneous frequency solution which are two parts of Hilbert-Huang Transformation are discussed in the thesis. Empirical mode decomposion is used to EEG which can be decomposed into a limited number of intrinsic mode functions. Different threshold are used to treat intrinsic mode functions to achieve de-noising. Results: Hilbert-Huang Transformation is demonstrated to be effective in removing the general EEG noise. Compared with the traditional wavelet transform, Hilbert-Huang Transform for EEG de-noising has some advantages. Conclusion: Using HHT method for EEG signals denoising effective and doable.
机译:测量时,许多噪音会融合到EEG信号中。为了有效地消除噪声,本文提出了一种基于希尔伯特·黄变换的新方法。本文讨论了Hilbert-Huang变换的两个部分,即经验模态分解和瞬时频率解的理论。 EEG使用经验模式分解,可以将EEG分解为有限数量的固有模式函数。使用不同的阈值来处理固有模式函数以实现降噪。结果:Hilbert-Huang变换被证明可以有效地消除一般的EEG噪声。与传统的小波变换相比,希尔伯特-黄变换在脑电信号降噪方面具有一定优势。结论:采用HHT方法对脑电信号进行消噪是可行的。

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