基于小波变换的脑电信号去噪

2017 International conference of Electronics, Communication and Aerospace Technology (ICECA) Pub Date : 2017-04-01 DOI:10.1109/ICECA.2017.8203645

Harender, Ranjan Sharma

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引用次数: 14

摘要

低幅值脑电信号容易受到各种噪声源的影响。本文提出了基于平稳小波变换(SWT)、通用阈值、统计阈值和离散小波变换(DWT)与symlet、haar、coif和bior4.4小波相结合的去噪方法。结果表明，该方法在信伪比(SAR)、相关系数(CC)和归一化均方误差(NMSE)等性能参数上有显著改善。利用Simulink对基于小波变换的脑电信号去噪进行建模，并利用Xilinx System Generator在FPGA上实现。

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EEG signal denoising based on wavelet transform

Low amplitude EEG signal are easily affected by various noise sources. This work presents de-noising methods based on the combination of stationary wavelet transform (SWT), universal threshold, statistical threshold and Discrete Wavelet Transform (DWT) with symlet, haar, coif, and bior4.4 wavelets. The results show significant improvement in performance parameter such as Signal to Artifacts ratio (SAR), Correlation Coefficient (CC) and Normalized Mean Squared error (NMSE). Simulink has been used to model DWT based de noising of EEG signal implementable on FPGA with Xilinx System Generator.

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2017 International conference of Electronics, Communication and Aerospace Technology (ICECA)

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