CAJ | 학술논문

在脑机接口(BCI)中,脑电信号(EEG)的特征提取和分类识别可以通过多层前馈神经网络的大量学习来实现,但是基于误差反向传播的BP神经网络标准算法收敛速度慢,在训练中效率不高,分类正确率也很有限.针对这些问题,文中提出使用一种快速稳定的Levenberg-Marquardt算法来代替BP算法进行神经网络的学习训练,并利用BCI 2008竞赛的Graz数据集B进行了对左右手想象运动脑电信号分类的MATLAB仿真实验.该方法使得脑电信号分类的正确率达到87.1%,比BP算法的正确率78.2%要高,并且具有更好的收敛性.该算法为脑电信号的分类提供了有效的手段.
재뇌궤접구(BCI)중,뇌전신호(EEG)적특정제취화분류식별가이통과다층전궤신경망락적대량학습래실현,단시기우오차반향전파적BP신경망락표준산법수렴속도만,재훈련중효솔불고,분류정학솔야흔유한.침대저사문제,문중제출사용일충쾌속은정적Levenberg-Marquardt산법래대체BP산법진행신경망락적학습훈련,병이용BCI 2008경새적Graz수거집B진행료대좌우수상상운동뇌전신호분류적MATLAB방진실험.해방법사득뇌전신호분류적정학솔체도87.1%,비BP산법적정학솔78.2%요고,병차구유경호적수렴성.해산법위뇌전신호적분류제공료유효적수단.
In the brain-computer interface ( BCI),the feature extraction and classification of electroencephalogram ( EEG) can be a-chieved by massive study of the multilayer feedforward neural network. But the BP neural network based on error back propagation con-verges slowly,and has low efficiency in training,limited accuracy in classification. To solve these problems,the quick and stable Leven-berg-Marquardt algorithm is adopted in this article instead of the BP algorithm to train the neural network. The MATLAB simulation ex-periment about classifying the EEG signals of the motor imagery of left hand and right hand uses the Graz data set B from the BCI com-petition 2008. The simulation results show that the accuracy rate of this algorithm is 87. 1%,which is superior to 78. 2% of the BP algo-rithm,and it converges better as well. This technology provides an effective way for EEG classification.