关键词: 卷积神经网络, 递归神经网络, 长短期记忆, 脑电信号, 分类识别

Abstract: Due to the low recognition rate of traditional EEG signal classification methods, and the exponentially decreasing recognition rate with the increase of the EEG signal categories, a multi-layered Convolution Bidirectional LSTM recursive neural network（CBLSTM） classification model is designed, aiming for the combination of temporal and spatial characteristics of EEG signals. This model extracts the frequency domain features of EEG sequences by using the multi-layer convolution neural network. It extracts the time domain features of EEG signals by the bidirectional LSTM. EEG signal sequences are input into this model frame by frame. And finally, the classification result can be output. The feasibility of this method is verified by comparative study. Experiments show that the average recognition rate of this classification model is improved , and the robustness is better.

Key words: convolutional neural network, recurrent neural networks, long-short term memory, Electroencephalography（EEG）, classification