基于脑电EEG的改进EEMD算法

发布时间：2018-02-14 22:55

  本文关键词： 集合经验模态分解 模态混叠 辅助噪声 信号估计　出处：《计算机科学》2017年05期 　论文类型：期刊论文

【摘要】：为了有效地改善模态混叠问题以适应脑电信号的研究,提出了一种改进的集合经验模态分解算法。首先对脑信号进行相关性筛选;然后自适应地从原始脑信号中预测脑电特性信号,融合高斯白噪声生成新型脑信号噪声;最后基于该噪声进行集合经验模态分解。仿真实验表明,新型脑信号噪声不仅具有自适应特性,而且可以更好地解决脑信号经验模态分解中的模态混叠问题,同时也证明了该算法在脑电研究领域的理论和应用价值。
[Abstract]:In order to effectively improve the modal aliasing problem to adapt to the study of EEG signals, an improved set empirical mode decomposition algorithm is proposed. Firstly, the correlation of brain signals is screened. Then the EEG characteristic signal is predicted from the original brain signal adaptively, and the new type of brain signal noise is generated by integrating Gao Si white noise. Finally, the set empirical mode decomposition is carried out based on the noise. The simulation results show that, The new brain signal noise not only has adaptive characteristics, but also can better solve the problem of modal aliasing in the empirical mode decomposition of brain signals. It also proves the theoretical and practical value of this algorithm in the field of EEG research.
【作者单位】： 南京邮电大学电子科学与工程学院;
【基金】：国家自然科学基金(61271082,61271334)资助
【分类号】：R741.044;TN911.7
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本文编号：1511820