Multi-lass Motion Image EEG Recognition Based on Gradient Lifting Tree
In this paper,to solve the four-way classification problem of motion image EEG signals,wavelet packet decomposi-tion and common space pattern are used to extract the features,and then gradient lifting decision tree classification is used to ana-lyze the extracted features.The gradient lifting decision tree can flexibly process data of various discrete values and continuous val-ues,and does not need to normalize the data,and can directly combine features and naturally process each missing value.Com-pared with other methods,the experimental results show that the classification accuracy of this method is 0.913 7.The experimental results show that the combination of feature extraction and classification method is effective and excellent.
gradient lifting decision treemotion image EEGwavelet decompositioncommon space model
万方数据
维普
