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.

Know Your Mind: Adaptive Cognitive Activity Recognition with Reinforced CNN Electroencephalography (EEG) signals reflect and measure activities in certain brain areas. Its zero clinical risk and easytouse features make it a good choice of providing insights into the cognitive process. However, effective analysis of timevarying EEG signals remains challenging. First, EEG signal processing and feature engineering are timeconsuming and highly rely on expert knowledge, and most existing studies focus on domainspecific classification algorithms, which may not apply to other domains. Second, EEG signals usually have low signaltonoise ratios and are more chaotic than other sensor signals. In this regard, we propose a generic EEGbased cognitive activity recognition framework that can adaptively support a wide range of cognitive applications to address the above issues. The framework uses a reinforced selective attention model to choose the characteristic information among raw EEG signals automatically. It employs a convolutional mapping operation to dynamically transform the selected information into a feature space to uncover the implicit spatial dependency of EEG sample distribution. We demonstrate the effectiveness of the framework under three representative scenarios: intention recognition with motor imagery EEG, person identification, and neurological diagnosis, and further evaluate it on three widely used public datasets. The experimental results show our framework outperforms multiple stateoftheart baselines and achieves competitive accuracy on all the datasets while achieving low latency and high resilience in handling complex EEG signals across various domains. The results confirm the suitability of the proposed generic approach for a range of problems in the realm of braincomputer Interface applications.