本文選題：腦機接口　切入點：運動想象　出處：《南昌大學學報(理科版)》2017年03期 　論文類型：期刊論文

【摘要】：為提高運動想象腦機接口的分類正確率,結(jié)合小波包分解與近似熵對腦電信號進行特征提取。該方法利用小波包對腦電信號全頻段進行分解,用近似熵函數(shù)對小波包結(jié)點提取分類特征,然后用稀疏表示對特征向量進行降維,最后使用功率差方法進行分類。實驗結(jié)果表明,在使用1秒數(shù)據(jù)進行分類的條件下,該方法在使用2種不同通道集合時都取得了很好的分類效果。使用32個和10個通道時分類正確率分別達到了95.65%和86.41%,比小波包分解與空域濾波方法分別提高了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)長度越短,分類識別率越高,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】： 南昌大學電子信息工程系;
【基金】：國家自然科學基金項目(61365013,61663025) 江西省教育廳科技項目(GJJ13054)
【分類號】：R318;TN911.7
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本文編號：1595093