首页|Research on recognition of O-MI based on CNN com-bined with SST and LSTM
Research on recognition of O-MI based on CNN com-bined with SST and LSTM
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Recognition algorithms have been widely used in brain computer interface(BCI)for neural paradigms classification.To improve the classification and recognition effect of motor imagery with motor observation(O-MI)in BCI rehabili-tation technology,this paper explores the function of convolutional neural network(CNN)combined with syn-chrosqueezed wavelet transform(SST)and long short-term memory(LSTM)in the recognition and classification of neural activities in the brain motor area.Combining the advantages of SST in signal feature extraction in the pretreat-ment stage and the ability of LSTM network in time series information modeling,the purpose is to make up for CNN's shortcomings in both aspects.This paper verifies the algorithm on the self-collected O-MI experimental datasets and the public datasets(BCI competition Ⅳ datasets 2a).The results show that the composite CNN algorithm incorporat-ing SST and LSTM achieves higher classification accuracy than classic algorithms and the similar new method which is CNN combined with discrete wavelet transform(DWT)and power spectral density(PSD),so it is convenient for practical application in O-MI BCI system.
LI Penghai、LIU Cong
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School of Integrated Circuit Science and Engineering,Tianjin University of Technology,Tianjin 300384,China
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