本文關(guān)鍵詞： 集合經(jīng)驗(yàn)?zāi)B(tài)分解 模態(tài)混疊 輔助噪聲 信號(hào)估計(jì)　出處：《計(jì)算機(jī)科學(xué)》2017年05期 　論文類型：期刊論文

【摘要】：為了有效地改善模態(tài)混疊問題以適應(yīng)腦電信號(hào)的研究,提出了一種改進(jìn)的集合經(jīng)驗(yàn)?zāi)B(tài)分解算法。首先對(duì)腦信號(hào)進(jìn)行相關(guān)性篩選;然后自適應(yīng)地從原始腦信號(hào)中預(yù)測(cè)腦電特性信號(hào),融合高斯白噪聲生成新型腦信號(hào)噪聲;最后基于該噪聲進(jìn)行集合經(jīng)驗(yàn)?zāi)B(tài)分解。仿真實(shí)驗(yàn)表明,新型腦信號(hào)噪聲不僅具有自適應(yīng)特性,而且可以更好地解決腦信號(hào)經(jīng)驗(yàn)?zāi)B(tài)分解中的模態(tài)混疊問題,同時(shí)也證明了該算法在腦電研究領(lǐng)域的理論和應(yīng)用價(jià)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.
【作者單位】： 南京郵電大學(xué)電子科學(xué)與工程學(xué)院;
【基金】：國家自然科學(xué)基金(61271082,61271334)資助
【分類號(hào)】：R741.044;TN911.7
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本文編號(hào)：1511820