Motor Imagery Classification Based on Plain Convolutional Neural Network and Linear Interpolation  

作　　者：LI Mingai WEI Lina 李明爱;魏丽娜

机构地区：[1]Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China [2]Beijing Key Laboratory of Computational Intelligence and Intelligent System,Beijing 100124,China

出　　处：《Journal of Shanghai Jiaotong university(Science)》2024年第6期958-966,共9页上海交通大学学报(英文版)

基　　金：Foundation item:the National Natural Science Foundation of China(Nos.62173010 and 11832003)。

摘　　要：Deep learning has been applied for motor imagery electroencephalogram(MI-EEG)classification in brain-computer system to help people who suffer from serious neuromotor disorders.The inefficiency network and data shortage are the primary issues that the researchers face and need to solve.A novel MI-EEG classification method is proposed in this paper.A plain convolutional neural network(pCNN),which contains two convolution layers,is designed to extract the temporal-spatial information of MI-EEG,and a linear interpolation-based data augmentation(LIDA)method is introduced,by which any two unrepeated trials are randomly selected to generate a new data.Based on two publicly available brain-computer interface competition datasets,the experiments are conducted to confirm the structure of pCNN and optimize the parameters of pCNN and LIDA as well.The average classification accuracy values achieve 90.27%and 98.23%,and the average Kappa values are 0.805 and 0.965 respectively.The experiment results show the advantage of the proposed classification method in both accuracy and statistical consistency,compared with the existing methods.

关 键 词：motor imagery CLASSIFICATION convolutional neural network data augmentation deep learning braincomputer interface 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TN911[自动化与计算机技术—控制科学与工程] R318[电子电信—通信与信息系统]