Application Of Attention-Based Gru Combined With Cnn Classification On P300 Signals

How to detect the collected P300 EEG signals more accurately is the core part of brain-computer interface technology. Aiming at the collected P300 EEG signals of the classical oddball paradigm, this paper proposed a fusion model based on CNN and RNN by using deep learning technology. This model adopts gated neural network GRU and combines attention mechanism to make the model better extract the time characteristics of EEG signals. At the same time, the model fuses the convolutional neural network to capture the spatial characteristics of EEG signals. The feature map extracted from the independent network is sent to the fully connected layer after splicing to obtain the classification results. Compared with some single convolutional neural networks, the proposed fusion model can better capture the time domain characteristics of signals. In terms of classification accuracy, the model proposed in this paper once reached 97.5%.