本文選題：腦磁圖(MEG) + 分類算法��； 參考：《計(jì)算機(jī)科學(xué)與探索》2017年05期

【摘要】：針對(duì)人腦對(duì)不同視覺(jué)目標(biāo)刺激產(chǎn)生的腦磁圖(magnetoencephalography,MEG)信號(hào),提出了一種新型的腦磁圖信號(hào)分類算法。該算法首先將濾波后的腦磁圖信號(hào)投影到新的特征空間,然后將腦磁圖信號(hào)投影后新特征的協(xié)方差特征投影到切線空間中,用協(xié)方差特征作為信號(hào)的特征,進(jìn)而對(duì)樣本進(jìn)行預(yù)分類;接著將預(yù)分類的樣本通過(guò)巴氏距離的調(diào)整,得到二次標(biāo)記結(jié)果;最后采用黎曼距離對(duì)協(xié)方差特征矩陣在流形上進(jìn)行調(diào)整,得到最終的分類結(jié)果。實(shí)驗(yàn)結(jié)果表明,該有監(jiān)督與無(wú)監(jiān)督相結(jié)合的算法有助于提高腦磁圖信號(hào)分類的準(zhǔn)確率。
[Abstract]:In view of the magnetoencephalography (MEG) signal generated by the human brain for different visual targets, a new classification algorithm for magnetoencephalography (magnetoencephalography) is proposed, which first projections the filtered brain magnetograph signals to the new feature space, and then projection the covariance features of the new features of the magnetoencephalography signals to the tangent space. The covariance feature is used as the characteristic of the signal, and then the sample is pre classified, and then the two marking results are obtained by adjusting the pre classified samples through the adjustment of the barren distance. Finally, the Riemann distance is used to adjust the covariance feature matrix on the manifold to get the final classification results. The experimental results show that it is supervised and unsupervised. The combined algorithm is helpful to improve the accuracy of classification of magnetoencephalography signals.
【作者單位】： 天津大學(xué)電子信息工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金Nos.61372145,61172121,61002030,61002027~~
【分類號(hào)】：R338;TP391.41

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本文編號(hào)：1946100