本文選題：腦機(jī)接口　切入點(diǎn)：運(yùn)動(dòng)想象　出處：《南昌大學(xué)學(xué)報(bào)(理科版)》2017年03期 　論文類型：期刊論文

【摘要】：為提高運(yùn)動(dòng)想象腦機(jī)接口的分類正確率,結(jié)合小波包分解與近似熵對(duì)腦電信號(hào)進(jìn)行特征提取。該方法利用小波包對(duì)腦電信號(hào)全頻段進(jìn)行分解,用近似熵函數(shù)對(duì)小波包結(jié)點(diǎn)提取分類特征,然后用稀疏表示對(duì)特征向量進(jìn)行降維,最后使用功率差方法進(jìn)行分類。實(shí)驗(yàn)結(jié)果表明,在使用1秒數(shù)據(jù)進(jìn)行分類的條件下,該方法在使用2種不同通道集合時(shí)都取得了很好的分類效果。使用32個(gè)和10個(gè)通道時(shí)分類正確率分別達(dá)到了95.65%和86.41%,比小波包分解與空域?yàn)V波方法分別提高了5.9%和8.32%,比傳統(tǒng)的共空域模式方法分別提高了7.18%和7.27%。另外,使用的數(shù)據(jù)長(zhǎng)度越短,分類識(shí)別率越高,表明該方法更適用于較短的數(shù)據(jù),有利于提高腦機(jī)接口的信息傳輸速度。
[Abstract]:In order to improve the classification accuracy of the motion-imaginary brain-computer interface, the wavelet packet decomposition and approximate entropy are combined to extract the features of the EEG signal, and the wavelet packet is used to decompose the whole frequency band of the EEG signal. The approximate entropy function is used to extract the classification feature of wavelet packet nodes, then the sparse representation is used to reduce the dimension of the feature vector, and the power difference method is used to classify the feature vector. The experimental results show that, under the condition that 1 seconds data is used for classification, The classification accuracy of 32 channels and 10 channels is 95.65% and 86.41 respectively, which is 5.9% and 5.9% higher than that of wavelet packet decomposition and spatial filtering, respectively. 8.32, which is 7.18% and 7.27 higher than the traditional common airspace mode, respectively. The shorter the length of the data used, the higher the classification recognition rate, which indicates that the proposed method is more suitable for shorter data and can improve the speed of BCI information transmission.
【作者單位】： 南昌大學(xué)電子信息工程系;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(61365013,61663025) 江西省教育廳科技項(xiàng)目(GJJ13054)
【分類號(hào)】：R318;TN911.7
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本文編號(hào)：1595093