ICA+CSSD的腦-機接口分類

針對復(fù)雜的腦電問題，介紹了一種對單次思維腦電信號提取、分類的方法。該方法的主要思想是將獨立成分分量和共空域子空間分解方法以及支持向量機學習方法結(jié)合起來，用于提取腦電信號特征。該方法分別被用于BCI Competition 2003 Data set IV和BCI Competition III Data set I，正確率分別達到了89%和92%。實驗證明獨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)合起來進行腦思維的分類，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－機接口; 腦電信號; 獨立分量分析; 支持向量機

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