腦機接口(BCI)技術是近年來國際上的研究熱點之一，它通常利用腦電(EEG)來實
現無動作的人機交互，運動想象是其中一種重要BCI 實驗范式，利用第二屆國際腦機接口競賽中的一組實驗數據為處理對象，將數據經公共空間模式濾波、小波時頻分解、然后采用T 加權提取最后特征，并利用支持向量機進行分類器設計。實驗結果表明，該算法效果較好，最終識別正確率達到89.3％。
關鍵詞：運動想象(MI)；公共空間模式(CSP)；支持向量機(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)