为提高运动想象脑机接口识别准确率,结合共空间模式(common spatial pattern,CSP)和卷积神经网络(convolutional neural network,CNN)方法,提出一种改进滤波器组共空间模式(filter bank common spatial pattern,FBCSP)和CNN的算法,用于多分类运动想象脑电信号识别任务.信号预处理后,使用包含重叠频带的FBCSP计算空间投影矩阵,数据经过投影得到更有区分度的特征序列.然后将特征序列以二维排列方式输入搭建的CNN模型中进行分类.所提出方法在脑机接口竞赛数据集2a和Ⅲa上验证,并和其他文献方法对比.结果表明,本文方法一定程度上提高了运动想象脑电信号的分类准确率,为运动想象研究提供了一个有效办法.
Classification of Motor Imagery EEG Signals Based on Improved FBCSP and CNN
In order to improve the recognition accuracy of motor imagery brain-computer interface,common spatial pattern(CSP)and convolutional neural network(CNN)methods were combined,and an improved filter bank common spatial pattern(FBCSP)and CNN algorithm was proposed for multi-classification motor imagery EEG signal recognition tasks.After the signal was preprocessed,the spatial projection matrix was calculated using the FBCSP containing the overlapping frequency band,and the data was projected to ob-tain a more discriminative feature sequence.Then the feature sequence was input into the constructed CNN model in a two-dimensional arrangement for classification.The proposed method was verified on the brain-computer interface competition datasets 2a and Ⅲa,and compared with other literature methods.The results show that the proposed method improves the classification accuracy of motor image-ry EEG signals to a certain extent,and provides an effective method for motor imagery research.
motor imageryEEG signalsbrain-computer interfacecommon spatial patternconvolutional neural network
万方数据
维普
