北京生物醫(yī)學(xué)工程

基于EMD和Hilbert變換的自發(fā)腦電信號特征提取

EEG Feature Extraction Based on Empirical Mode Decomposition and Hilbert Transform in Brain Computer Interface

作者：吳婷顏國正錢炳鋒

單位：上海電機大學(xué)機械學(xué)院（上海200240）

關(guān)鍵詞：腦機接口；腦電信號；經(jīng)典模態(tài)分解；希爾伯特變換；特征提取

分類號：

出版年·卷·期（頁碼）：2011·30·4（381-386）

摘要：

在腦機接口研究中，針對腦電信號的特征提取，提出一種基于EMD的Hilbert變換的方法。在變換過程中根據(jù)信號的局部特征自動選擇基函數(shù)，求得信號在每個時間段的希爾波特譜；以時頻窗口內(nèi)的統(tǒng)計特性作為特征，利用Fisher距離選擇最佳特征集輸入分類器。最后利用BCI 2003競賽數(shù)據(jù)，通過對特征矢量的可分性和識別精度兩個指標(biāo)的評估，表明了所提出方法的有效性。

In the study of brain computer interfaces，a method based on empirical mode decomposition(EMD) and Hilbert transformation was proposed. The method was used for the feature extraction of electroencephalogram. In this method，the basis function was selected automatically according to the local features of signal during the transforming process，the Hilbert spectrum was obtained in each period，and the statistical characteristics in time-frequency window were considered as features. Then the optimal feature sets were formed by the Fisher distance rule and put into the classification. The performance of the eigenvector was evaluated by separability and recognition accuracy with the data set of BCI 2003 competition，and classification results proved the effectiveness of the proposed method.

參考文獻：

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