A Novel Multiscale Dilated Convolution Neural Network With Gating Mechanism for Decoding Driving Intentions Based on EEG

Deep learning methods based on convolution neural networks (CNNs) have achieved good classification performance in decoding electroencephalography (EEG). In this article, a novel framework combining-gating mechanism and dilated CNN (GDCNN) is proposed for decoding EEG signals evoked by four different driving intentions. GDCNN provides different receptive fields and controls the information flow between convolution layers, which help to detect different sizes of information in EEG signals. The proposed method reaches accuracies of 93.17% and 73.33% for the subject-dependent and subject-independent experiments, outperforming several benchmark methods. The data augmentation that randomly concatenates multitrial EEG sequences is adopted to promote generalization of decoding model. This strategy effectively prevents overfitting and improves the decoding accuracies of EEGNet, DeepConvNet, and GDCNN by 4.3%, 4.75%, and 3.92%, respectively. These results indicate GDCNN is beneficial for decoding EEG and it has application potential in the brain–computer interface (BCI) systems based on video stimuli.