SCNet: A spatial feature fused convolutional network for multi-channel EEG pathology detection

Abstract

Deep learning models, especially convolutional neural networks (CNNs), have shown remarkable achievements in clinical electroencephalography (EEG) pathology detection, due to their prominent capability in automatic feature learning. However, most existing CNN-based models ignore the spatial correlations of the EEG signals and the latent complementary information provided by different convolutional layers, which is essentially an important clue for pathology detection. To this end, we propose SCNet, a Spatial Feature Fused Convolutional Network, for multi-channel EEG pathology detection. SCNet first designs a spatial information learning mechanism to capture the channel-wise spatial correlations via global pooling strategies. A multi-level feature fusion module, which combines feature maps learned by different convolutional layers, is then devised to fully leverage the complementarity of multi-level features. The efficacy of the proposed SCNet is experimentally evaluated on both datasets, and the results obtained outperform current representatives in the literature. Ablation studies on the spatial information learning and multi-level fusion modules further confirm their great potential in EEG-based diagnostic applications.