本文選題：通道選擇 + 獨(dú)立成分分析　； 參考：《計(jì)算機(jī)工程與科學(xué)》2017年09期

【摘要】：針對腦電信號存在個體差異性并易受噪聲、偽跡干擾的特點(diǎn),提出一種基于獨(dú)立成分分析ICA的優(yōu)選特征通道算法。采用ICA將通道的數(shù)據(jù)分解為N200、P300、眼電偽跡以及其他生理信號,根據(jù)這些信號對每個通道的影響程度,判定各通道是否適合進(jìn)行特征提取。分別采用本方法和三種常用方法對12個被試的腦電數(shù)據(jù)進(jìn)行特征通道選擇,并進(jìn)行N200和P300電位的辨識,經(jīng)比對發(fā)現(xiàn),本文方法取得了93.10%的平均分類準(zhǔn)確率,比其他三種方法下的準(zhǔn)確率分別高出7.27%、1.07%和75.96%。為預(yù)測任意被試的最優(yōu)通道,采用最小二乘法對ICA權(quán)值和通道選擇閾值之間的關(guān)系進(jìn)行擬合,對三個新被試進(jìn)行最優(yōu)通道預(yù)測和電位的辨識,得到較高的分類準(zhǔn)確率,說明此預(yù)測方法具有一定普適性。
[Abstract]:Aiming at the characteristics of individual difference, noise and artifact interference in EEG signals, an ICA based optimal feature channel selection algorithm is proposed. ICA is used to decompose the channel data into N200P300, eye electrical artifacts and other physiological signals. According to the influence of these signals on each channel, it is determined whether each channel is suitable for feature extraction. This method and three common methods were used to select the characteristic channels of EEG data of 12 subjects, and the potential of N200 and P300 were identified. By comparison, it was found that the average classification accuracy of this method was 93.10%. The accuracy was 7.27% and 75.96% higher than that of the other three methods, respectively. In order to predict the optimal channel of arbitrary subjects, the relationship between ICA weight and channel selection threshold was fitted by the least square method, and the optimal channel prediction and potential identification were carried out on three new subjects, and a higher classification accuracy was obtained. It shows that this prediction method has certain universality.
【作者單位】： 天津大學(xué)電氣與自動化工程學(xué)院;加州州立大學(xué)貝克斯菲爾德分校計(jì)算機(jī)系&電氣工程與計(jì)算機(jī)科學(xué)學(xué)院;
【基金】：國家自然科學(xué)基金(61473207)
【分類號】：R318;TN911.6

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