腦機(jī)接口系統(tǒng)中EEG 信號(hào)特征提取與分類(lèi)Feature Ex

腦機(jī)接口(BCI)技術(shù)是近年來(lái)國(guó)際上的研究熱點(diǎn)之一，它通常利用腦電(EEG)來(lái)實(shí)
現(xiàn)無(wú)動(dòng)作的人機(jī)交互，運(yùn)動(dòng)想象是其中一種重要BCI 實(shí)驗(yàn)范式，利用第二屆國(guó)際腦機(jī)接口競(jìng)賽中的一組實(shí)驗(yàn)數(shù)據(jù)為處理對(duì)象，將數(shù)據(jù)經(jīng)公共空間模式濾波、小波時(shí)頻分解、然后采用T 加權(quán)提取最后特征，并利用支持向量機(jī)進(jìn)行分類(lèi)器設(shè)計(jì)。實(shí)驗(yàn)結(jié)果表明，該算法效果較好，最終識(shí)別正確率達(dá)到89.3％。
關(guān)鍵詞：運(yùn)動(dòng)想象(MI)；公共空間模式(CSP)；支持向量機(jī)(SVM)
Abstract: Brain-computer interface (BCI) is one kind of techniques on which more and more interest has been put by the international researchers recently. By using electroencephalogram, BCI establishes the interfaces between human and computer，in which motor imagery is an? important experimental paradigm. In this paper, the dataset from BCI Competition II is analyzed using the algorithm designed through common spatial pattern filter, continuous wavelet transform decomposes , then to use the T-weighted extraction final characteristic, and using support vector machines to design the classifier. By means of the above approaches, better classification results are obtained. The result on testing set revealed an accuracy of 89.3% for classification.
Key words: brain-computer interface (BCI); electroencephalogram (EEG); motor imagery (MI);
common spatial pattern (CSP); support vector machines (SVM)