針對(duì)復(fù)雜的腦電問題，介紹了一種對(duì)單次思維腦電信號(hào)提取、分類的方法。該方法的主要思想是將獨(dú)立成分分量和共空域子空間分解方法以及支持向量機(jī)學(xué)習(xí)方法結(jié)合起來(lái)，用于提取腦電信號(hào)特征。該方法分別被用于BCI Competition 2003 Data set IV和BCI Competition III Data set I，正確率分別達(dá)到了89%和92%。實(shí)驗(yàn)證明獨(dú)立成分分量算法和共空域子空間分解方法能夠很好地結(jié)合起來(lái)進(jìn)行腦思維的分類，分類正確率很高，是一種快速、穩(wěn)定可行的分解方法。
關(guān) 鍵 詞 腦－機(jī)接口; 腦電信號(hào); 獨(dú)立分量分析; 支持向量機(jī)

Abstract Identification and classification technology plays an important part in study of the brain-computer interface (BCI) system. In this paper, an algorithm is presented to deals with the complex brain signals and extract features and classify single-trial electroencephalogram (EEG). The algorithm combines independent component analysis algorithm and common spatial subspace decomposition with support vector machine to extract features from multi-channel EEG and electro cortico gram (ECoG). This algorithm was applied to the results of data analysis show that the proposed method can classify with high accuracy.
Key words brain-computer interface; electroencephalograms; independent component analysis (ICA); support vector machine