本文選題：多特征　切入點(diǎn)：BP神經(jīng)網(wǎng)絡(luò)　出處：《電子技術(shù)應(yīng)用》2017年09期

【摘要】：研究了一種基于運(yùn)動(dòng)想象識(shí)別的腦-機(jī)接口(BCI)系統(tǒng),通過提取想象過程中的腦電信號(hào)(EEG)中Alpha波特征,采用多特征分類的方法,以提高腦-機(jī)接口系統(tǒng)運(yùn)動(dòng)想象識(shí)別的正確率。針對(duì)腦電信號(hào)單特征分類精確度低、耗時(shí)長(zhǎng)等缺點(diǎn),采用自回歸模型法、統(tǒng)計(jì)特征提取和頻域分析的方法對(duì)Alpha波提取多個(gè)特征值,利用BP神經(jīng)網(wǎng)絡(luò)進(jìn)行分類,對(duì)運(yùn)動(dòng)想象進(jìn)行識(shí)別。通過實(shí)驗(yàn)驗(yàn)證了其識(shí)別率較高,取得了預(yù)期的效果,證明了多特征融合結(jié)合BP神經(jīng)網(wǎng)絡(luò)運(yùn)用于腦機(jī)接口系統(tǒng)的可行性。
[Abstract]:In this paper, a brain-computer interface (BCI) system based on motion imagination recognition is studied. By extracting the features of Alpha wave in the process of imagining, the method of multi-feature classification is adopted. In order to improve the correct rate of motion imagination recognition in brain-computer interface system, the autoregressive model method is adopted to solve the disadvantages of low accuracy and long time consuming in single feature classification of EEG signals. The methods of statistical feature extraction and frequency domain analysis are used to extract multiple eigenvalues of Alpha wave, and BP neural network is used to classify and recognize the motion imagination. The experimental results show that the recognition rate is high and the expected results are obtained. The feasibility of applying multi-feature fusion and BP neural network to the brain-computer interface system is proved.
【作者單位】： 吉林大學(xué)儀器科學(xué)與電氣工程學(xué)院;
【分類號(hào)】：R318;TN911.7;TP183

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相關(guān)期刊論文 前1條

1 高上凱;;神經(jīng)工程與腦-機(jī)接口[J];生命科學(xué);2009年02期

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本文編號(hào)：1697467