Classify EEG and Reveal Latent Graph Structure with SpatioTemporal Graph Convolutional Neural Network Electroencephalogram(EEG) is a test that detect brain activities using multiple electrodes placed on the scalp. Multiple channels of EEG signals are recorded through the electrodes and are widely used in applications such as neurological disease diagnosis, emotion recognition, and behavior modeling. Recently, deep learning methods have been applied to classify EEG signals, where the different EEG channels are almost treated as a 2D grid input to the machine learning model. This data formation doesnu0027t consider The complex connection among the EEG channels is not considered in such data formation. In our work, we treat EEG signals as frames of graph, and propose an endtoend edgeaware spatiotemporal graph convolutional neural network for EEG classification. Specifically, we iteratively apply graph convolutional layer spatially and standard convolutional layer temporally. Since there is no prior knowledge about the exact connection among EEG channels, in our model, we initialize the connection as complete graph and apply learnable mask to capture graph structure at different levels. Furthermore, we also propose an iterative method based on information aggregation in graph convolution mechanism to reveal the latent graph structure. Empirical evaluation shows that our model achieves superior performance over stateoftheart methods for EEG classification, and the learnt and revealed latent EEG graph structure is verified to be meaningful by neuroscientists.

Learning Attentional Temporal Cues of Brainwaves with Spatial Embedding for Motion Intent Detection As brain dynamics fluctuate considerably across different subjects, it is challenging to design effective handcrafted features based on prior knowledge. Regarding this gap, this paper proposes a Graphbased Convolutional Recurrent Attention Model (GCRAM) to explore EEG features across different subjects for movement intention recognition. A graph structure is first developed to embed the positioning information of EEG nodes, and then a convolutional recurrent attention model learns EEG features from both spatial and temporal dimensions and adaptively emphasizes on the most distinguishable temporal periods. The proposed approach is validated on two public movement intention EEG datasets. The results show that the GCRAM achieves superior performance to stateoftheart methods regarding recognition accuracy and ROCAUC. Furthermore, model interpreting studies reveal the learning process of different neural network components and demonstrate that the proposed model can extract detailed features efficiently.