本文選題：駕駛疲勞 + 核主元分析�。� 參考：《北京工業(yè)大學(xué)學(xué)報(bào)》2017年06期

【摘要】：為有效識(shí)別駕駛員疲勞狀態(tài),基于腦電信號(hào)(electroencephalogram,EEG)提出了一種駕駛疲勞狀態(tài)識(shí)別方法.首先,以時(shí)間段劃分疲勞等級(jí),并采用主、客觀測(cè)評(píng)指標(biāo)對(duì)疲勞等級(jí)劃分的合理性進(jìn)行驗(yàn)證.然后,利用快速傅里葉變換對(duì)腦電信號(hào)進(jìn)行分析,在此基礎(chǔ)上選取3種頻段的平均幅值和5項(xiàng)合成指標(biāo),通過(guò)核主元分析(kernel principal component analysis,KPCA)構(gòu)建疲勞識(shí)別腦電指標(biāo),結(jié)合支持向量機(jī)(support vector machine,SVM),構(gòu)建了駕駛員疲勞狀態(tài)識(shí)別模型.最后,采用30名駕駛員連續(xù)駕駛2 h的腦電數(shù)據(jù),對(duì)該模型方法進(jìn)行試算.試算結(jié)果表明:疲勞狀態(tài)識(shí)別正確率為79.17%~92.03%,平均正確率為84.62%,該方法可用于駕駛疲勞識(shí)別.
[Abstract]:In order to identify driver fatigue state effectively, a driving fatigue state recognition method based on EEG electroencephalogramma (EGG) is proposed. First, the fatigue grade is divided by time, and the rationality of fatigue grade is verified by subjective and objective indexes. Then, the EEG signal is analyzed by using fast Fourier transform. On the basis of this, the average amplitude of three frequency bands and five synthetic indexes are selected, and the fatigue identification EEG index is constructed by kernel principal component analysis (kernel principal component analysis) and kernel principal component analysis (KPCA). Combined with support vector machine (SVM), a driver fatigue state recognition model is constructed. Finally, the EEG data of 30 drivers driving for 2 hours were used to calculate the model. The experimental results show that the correct rate of fatigue state recognition is 79.17 and 92.03, and the average correct rate is 84.622.This method can be used for driving fatigue recognition.
【作者單位】： 西南交通大學(xué)交通運(yùn)輸與物流學(xué)院;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(51108390) 國(guó)家重點(diǎn)研發(fā)計(jì)劃資助課題(2016YFC0802209)
【分類號(hào)】：R318;TN911.7

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1 石喬莉;王磊;耿旭婧;葛偉豪;王洋;邊京華;李穎;;基于腦電信號(hào)的駕駛疲勞狀態(tài)分析[A];天津市生物醫(yī)學(xué)工程學(xué)會(huì)第三十一屆學(xué)術(shù)年會(huì)論文集[C];2011年

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