Target EEG recognition based on CNN and bidirectional GRU hybrid model

Abstract

To improve the recognition accuracy of target EEG signals, a classification model based on the combination of Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) is proposed. CNN is used to extract the frequency domain and space domain features of EEG signals, which is connected to bidirectional GRU after the fully connected layer to continue mining the deep timing information of the data, and finally the softmax layer is used to classify the EEG data into target and non-target signals. The model obtained an average classification accuracy of 95.88% on the UC San Diego Rapid Serial Visual Presentation (RSVP) EEG target detection dataset, outperforming the comparison method. It is shown that the proposed method can effectively extract the feature information of the target EEG signal and improve the EEG signal classification accuracy.