本文關(guān)鍵詞： 腦電信號(hào) 自適應(yīng)小波閾值 經(jīng)驗(yàn)?zāi)B(tài)分解 模糊熵 人工蜂群 支持向量機(jī)　出處：《杭州電子科技大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：腦-機(jī)接口(Brain-Computer Interface,BCI)是一種基于腦電信號(hào)(Electroencephalogram,EEG)的人機(jī)交互方式,它能夠不需要依靠周邊的肌肉和神經(jīng)組織,只依靠腦部信號(hào)便可以達(dá)到自由行動(dòng)及與外界溝通等目的。本文以基于運(yùn)動(dòng)想象的腦電信號(hào)為研究對(duì)象,對(duì)EEG信號(hào)預(yù)處理、特征提取和模式分類等處理過程進(jìn)行研究,本文主要完成的研究工作如下:(1)預(yù)處理:為了減少信號(hào)中的噪聲和其它的干擾成分,本文利用自適應(yīng)的小波閾值去噪算法對(duì)采集的腦電信號(hào)進(jìn)行處理。實(shí)驗(yàn)采用BCI國(guó)際競(jìng)賽的數(shù)據(jù)集,結(jié)果表明該方法在很大程度上減少了噪聲干擾。(2)特征提取:由于腦電信號(hào)的非線性特點(diǎn),本文提出了將改進(jìn)的經(jīng)驗(yàn)?zāi)B(tài)分解方法與模糊熵算法相結(jié)合的特征提取方法。原始的腦電信號(hào)經(jīng)過分解得到若干個(gè)固有模態(tài)分量(IMF),然后利用互信息篩選出有用的IMF分量并進(jìn)行重構(gòu),最后利用模糊熵對(duì)其進(jìn)行特征提取,并采用2008年的BCI競(jìng)賽數(shù)據(jù)驗(yàn)證算法的特征提取效果。(3)模式分類:當(dāng)采用支持向量機(jī)(SVM)進(jìn)行模式分類時(shí),核參數(shù)g和懲罰因子C的選擇關(guān)系著分類器的性能,所以本文利用人工蜂群算法(ABC)優(yōu)化SVM的模型參數(shù),以增強(qiáng)分類器的分類性能,并對(duì)提取的腦電信號(hào)特征進(jìn)行模式分類。(4)實(shí)驗(yàn)結(jié)果與分析:實(shí)驗(yàn)采用2005年和2008年BCI國(guó)際競(jìng)賽的數(shù)據(jù)集,實(shí)驗(yàn)一對(duì)比了ABC算法與傳統(tǒng)優(yōu)化算法的尋優(yōu)性能,證明了ABC算法具有良好的尋優(yōu)性能。實(shí)驗(yàn)二的結(jié)果表明ABC算法優(yōu)化后增強(qiáng)了SVM的分類性能,有效地提高了腦電信號(hào)的分類正確率。實(shí)驗(yàn)三利用改進(jìn)的EMD和模糊熵算法進(jìn)行特征提取,然后結(jié)合人工蜂群優(yōu)化的支持向量機(jī)對(duì)提取的特征進(jìn)行分類,由實(shí)驗(yàn)結(jié)果可知,改進(jìn)EMD和模糊熵算法相結(jié)合的方法是有效的,經(jīng)ABC優(yōu)化的SVM得到的識(shí)別率比傳統(tǒng)的SVM要高。
[Abstract]:Brain-Computer Interface (BCI) is a kind of electroencephalogram based on electroencephalogram (EEG). EGG is a human-computer interaction that allows it to rely on peripheral muscles and neural tissue without needing to rely on it. We can only rely on the brain signal to achieve the goal of free movement and communication with the outside world. In this paper, we preprocess the EEG signal with the EEG signal based on motion imagination as the research object. The main work of this paper is as follows: preprocessing: in order to reduce the noise and other interference components in the signal. In this paper, we use the adaptive wavelet threshold denoising algorithm to process the collected EEG signals. The experiment adopts the data set of BCI international competition. The results show that this method greatly reduces the noise interference. In this paper, an improved empirical mode decomposition (EMD) method combined with fuzzy entropy algorithm is proposed. The original EEG signal is decomposed to obtain a number of intrinsic modal components (IMF). Then the useful IMF components are selected by mutual information and reconstructed, and the feature extraction is carried out by fuzzy entropy. And the feature extraction effect of the BCI contest data validation algorithm of 2008 is used to classify the pattern: when using support vector machine (SVM) to classify the pattern. The selection of kernel parameter g and penalty factor C is related to the performance of classifier, so the artificial bee colony algorithm is used to optimize the model parameters of SVM in order to enhance the classification performance of the classifier. The experimental results and analysis were as follows: the data sets of BCI international competition on 2005 and 2008 were used in the experiment. Experiment 1 compares the optimization performance of ABC algorithm with that of traditional optimization algorithm, and proves that ABC algorithm has good optimization performance. Experiment 2 shows that ABC algorithm improves the classification performance of SVM after optimization. Experiment 3 uses improved EMD and fuzzy entropy algorithm to extract features, and then combines the support vector machine of artificial bee colony optimization to classify the extracted features. The experimental results show that the combination of improved EMD and fuzzy entropy algorithm is effective, and the recognition rate of SVM optimized by ABC is higher than that of traditional SVM.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R318;TN911.7

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