| 基于小波包变换的脑电波信号降噪及特征提取 |
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| 引用本文： | 刘珑,李胜,王轶卿. 基于小波包变换的脑电波信号降噪及特征提取[J]. 计算机工程与科学, 2015, 37(4): 790-795 |
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| 作者姓名： | 刘珑 李胜 王轶卿 |
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| 作者单位： | 1. 南京理工大学自动化学院,江苏南京,210094
2. 南京工业大学自动化与电气工程学院,江苏南京,210009 |
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| 基金项目： | 国家自然科学基金资助项目(51175266);江苏省高校自然科学基金资助项目(12KJB510008);江苏省普通高校研究生科研创新计划资助项目(CXZZ13 0207) |
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| 摘 要： | 针对原始脑电波信号存在非平稳性且非常容易受到各种信号干扰等特点,对基于小波变换和小波包变换的脑电波信号的滤波降噪方法,和基于小波包变换的脑电波信号特征提取方法进行了研究。首先利用MindSet采集到原始脑电波数据,然后分别应用小波变换和小波包变换对其进行降噪处理,比较了两种方法的性能,验证了基于小波包变换的降噪方法的优越性和特征提取方法的有效性。
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| 关 键 词： | 脑电波信号 降噪 特征提取 小波变换 小波包变换 |
| 收稿时间： | 2014-01-13 |
| 修稿时间： | 2014-05-21 |
EEG signal denoising and feature extraction based on wavelet packet transform |
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| LIU Long , LI Sheng , WANG Yi-qing. EEG signal denoising and feature extraction based on wavelet packet transform[J]. Computer Engineering & Science, 2015, 37(4): 790-795 |
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| Authors: | LIU Long LI Sheng WANG Yi-qing |
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| Affiliation: | (1.School of Automation,Nanjing University of Science and Technology,Nanjing 210094;2.School of Automation and Electrical Engineering,Nanjing University of Technology,Nanjing 210009,China) |
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| Abstract: | Since original EEG signals are non-stationary and very vulnerable to a variety of signal interference,we propose a method for EEG signal denoising based on wavelet transform and wavelet packet transform, and a method for EEG signal feature extraction based on wavelet packet transform.First,we use MindSet headset to collect original EEG data and denoise the data by wavelet transform and wavelet packet transform respectively. We compare the two methods,and the results prove the superiority of the proposed denoising method and the validity of the feature extraction method based on wavelet packet transform. |
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| Keywords: | EEG signal denoising feature extraction wavelet transform wavelet packet transform |
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