本文選題：腦電信號　切入點：特征提取　出處：《自動化學(xué)報》2017年11期 　論文類型：期刊論文

【摘要】：正則化共空間模式(Regularized common spatial pattern,RCSP)解決了共空間模式(Common spatial pattern,CSP)對噪聲敏感的問題,但它在小樣本腦電數(shù)據(jù)集中的表現(xiàn)并不理想.針對上述問題,本文提出了Bagging RCSP(BRCSP)算法,通過Bagging方法重復(fù)選取訓(xùn)練數(shù)據(jù)來構(gòu)造一個個包,并提取RCSP特征,再利用線性判別分析(Linear discriminant analysis,LDA)將特征向量映射到低維空間中,最后采用最近鄰(Nearest neighborhood classifier,NNC)算法判定分類結(jié)果.線下實驗證明,相比較聚合正則化共空間模式(RCSP with aggregation,RCSP-A),BRCSP的平均準確率提高了2.92%,且方差更小,魯棒性更好.最后,在智能輪椅平臺上,10位受試者利用BRCSP算法實現(xiàn)左右手運動想象腦電信號控制輪椅完成"8"字形路徑的實驗,證明了該算法在腦電信號特征提取中的有效性.
[Abstract]:Regularized common spatial pattern (RCSP) has solved the noise sensitivity problem of Common spatial pattern (CSP), but its performance in small sample EEG data sets is not satisfactory. In view of the above problems, a Bagging RCSP algorithm is proposed in this paper. The Bagging method is used to repeatedly select training data to construct packets and extract RCSP features. Then linear discriminant analysis (LDA) is used to map feature vectors to low dimensional space. In the end, the nearest neighbor neighborhood classifier is used to judge the classification results. The offline experiments show that the average accuracy of RCSP with aggregation is increased by 2.92%, and the variance is smaller and the robustness is better than that of RCSP with aggregation. On the platform of intelligent wheelchair, 10 subjects use BRCSP algorithm to realize the left and right hand motion imagination EEG control wheelchair to complete the "8" zigzag path experiment, which proves the effectiveness of the algorithm in EEG feature extraction.
【作者單位】： 重慶郵電大學(xué)先進制造工程學(xué)院;重慶郵電大學(xué)自動化學(xué)院;
【基金】：重慶市科學(xué)技術(shù)委員會項目(cstc2015jcyjBX0066,cstc2017jcyjAX0033) 重慶市教委科學(xué)技術(shù)項目(KJ1600428)資助~~
【分類號】：R318;TN911.7

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