本文選題：深度學習 + 卷積神經(jīng)網(wǎng)絡�。� 參考：《計算機學報》2017年06期

【摘要】：基于運動想象的腦機接口技術已經(jīng)廣泛的應用于康復外骨骼領域.由于腦電信號的信噪比低,使得腦機接口分類率很難提高.因此,有效的腦電特征提取與分類方法成為現(xiàn)在的研究熱點.該文創(chuàng)新地采用基于深度學習理論的卷積神經(jīng)網(wǎng)絡對單次運動想象腦電信號進行特征提取和分類.首先,根據(jù)腦電信號時間和空間特征相結(jié)合的特性,針對性地設計了一個5層的CNN結(jié)構(gòu)來進行運動想象分類;其次,基于想象左手運動和腳運動設計了運動想象實驗范式,獲得運動想象實驗數(shù)據(jù);再次,將該方法應用于公共數(shù)據(jù)集和實驗數(shù)據(jù)集并建立分類模型,同時與其它3種方法(功率值+SVM、CSP+SVM和MRA+LDA)相比較;最后,將從實驗數(shù)據(jù)集中獲得的分類模型(具有最好分類表現(xiàn))應用于上肢康復外骨骼的實時控制中,驗證該文提出方法的可行性.實驗結(jié)果表明,卷積神經(jīng)網(wǎng)絡方法可以提高分類識別率:卷積神經(jīng)網(wǎng)絡方法應用在公共數(shù)據(jù)集(90.75%±2.47%)和實驗數(shù)據(jù)集(89.51%±2.95%)中的平均識別率均高于其它3種方法;在上肢康復外骨骼的實時控制中,也驗證了CNN方法的可行性:所有被試的平均識別率為88.75%±3.42%.該文提出的方法可實現(xiàn)運動想象的精確識別,為腦機接口技術在康復外骨骼領域的應用提供了理論基礎與技術支持.
[Abstract]:The brain-computer interface technology based on motion imagination has been widely used in the field of rehabilitation exoskeleton. Because of the low signal-to-noise ratio (SNR) of EEG signals, it is difficult to improve the classification rate of BCI. Therefore, the effective method of EEG feature extraction and classification has become a hot topic. In this paper, a convolution neural network based on depth learning theory is used to extract and classify the feature of a single motion imaginary EEG signal. Firstly, according to the characteristics of EEG time and space, a five-layer CNN structure is designed to classify motion imagination. Secondly, based on the imagination of left hand motion and foot movement, a motion imagination experimental paradigm is designed. The experimental data of motion imagination are obtained. Thirdly, the method is applied to the common data set and experimental data set, and the classification model is established. At the same time, it is compared with the other three methods (the power value SVMN CSP SVM and the MRA LDAs). Finally, the proposed method is applied to the common data set and the experimental data set. The classification model (with the best classification performance) obtained from the experimental data set is applied to the real-time control of exoskeleton in upper limb rehabilitation, and the feasibility of the proposed method is verified. The experimental results show that convolution neural network method can improve the classification recognition rate: the average recognition rate of convolution neural network method is higher than that of the other three methods in common data set (90.75% 鹵2.47%) and experimental data set (89.51% 鹵2.95%). In the real-time control of exoskeleton for upper limb rehabilitation, the feasibility of CNN method was also verified: the average recognition rate of all subjects was 88.75% 鹵3.42. The method proposed in this paper can realize the accurate recognition of motion imagination and provide the theoretical basis and technical support for the application of brain-computer interface technology in the field of rehabilitation exoskeleton.
【作者單位】： 浙江大學計算機科學與技術學院;
【基金】：國家自然科學基金(61303137) 中國博士后科學基金(2015M581935) 浙江省博士后科學基金(BSH1502116) 浙江省科技計劃項目(2015C31051,2016C33139)資助~~
【分類號】：R49;TN911.7;TP183

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