The subject proposes a system for recognizing and classifying motor imagery signals by dividing the brain-computer interface data into time and frequency domains.The EEG signal data of forward,stop,left-turn,and right-turn motor imagery of four subjects were collected as the experimental dataset,and two deep learning models were proposed to compare the signal recognition accuracy,the two deep learning models are:gated recurrent unit neural network(GRU)and a hybrid deep learning framework 1DCNN-GRU model.The unprocessed EEG signals were also subjected to Fast Fourier Transform to extract the important feature values of the data,and the experimental dataset was subjected to 6:2:2 train-validate-test segmentation.
关键词
脑机接口/运动想象/门控循环单元/混合深度学习框架
Key words
brain-computer interface/motor imagery/gated recurrent unit/hybrid deep learning framework
维普
万方数据