本文關(guān)鍵詞： 腦電 情緒識(shí)別 漂移數(shù)據(jù) 擬合求差 支持向量機(jī)　出處：《華中科技大學(xué)學(xué)報(bào)(自然科學(xué)版)》2017年09期 　論文類型：期刊論文

【摘要】：本研究針對(duì)腦電信號(hào)在采集過程中出現(xiàn)的漂移情況,采用支持向量機(jī)分類器,分析了節(jié)律對(duì)數(shù)功率、分形維數(shù)和信號(hào)熵等9種特征,研究了腦電漂移數(shù)據(jù)對(duì)情緒分類的影響;同時(shí),采用擬合求差的方法,嘗試對(duì)腦電漂移數(shù)據(jù)進(jìn)行校正.實(shí)驗(yàn)結(jié)果表明:腦電漂移數(shù)據(jù)會(huì)導(dǎo)致情緒分類正確率下降,而擬合求差法可以在一定程度上補(bǔ)償漂移數(shù)據(jù)對(duì)分類造成的不利影響.仿真結(jié)果顯示:不存在漂移數(shù)據(jù)時(shí),樣本熵和θ節(jié)律功率對(duì)數(shù)兩種特征的情緒分類效果最好,而存在未經(jīng)校正的漂移數(shù)據(jù)時(shí),δ節(jié)律功率對(duì)數(shù)特征的情緒分類結(jié)果最好;漂移數(shù)據(jù)校正后,樣本熵和δ節(jié)律功率對(duì)數(shù)兩種特征的情緒分類結(jié)果最好.
[Abstract]:In this study, support vector machine (SVM) classifier is used to analyze 9 features of rhythm logarithmic power, fractal dimension and signal entropy, and the influence of EEG drift data on emotion classification is studied. At the same time, the fitting method is used to correct the EEG drift data. The experimental results show that the EEG drift data will lead to a decrease in the correct rate of emotion classification. However, the fitting method can compensate for the adverse effect of drift data on classification to some extent. The simulation results show that when there is no drift data, the classification effect of sample entropy and 胃 rhythm power logarithm is the best. When there are uncorrected drift data, the emotion classification results of 未 rhythm power logarithm feature are the best, and after drift data correction, sample entropy and 未 rhythm power logarithm feature have the best emotion classification results.
【作者單位】： 北京航空航天大學(xué)電子信息工程學(xué)院;中國空空導(dǎo)彈研究院;
【基金】：高等學(xué)校博士學(xué)科點(diǎn)專項(xiàng)科研基金資助項(xiàng)目(20121102130001) 國家自然科學(xué)基金資助項(xiàng)目(61603013)
【分類號(hào)】：R318;TN911.7
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本文編號(hào)：1538359