MindReading: An UltraLowPower Photonic Accelerator for EEGbased Human Intention Recognition. A scalprecording electroencephalography (EEG)based braincomputer interface (BCI) system can greatly improve the quality of life for people who suffer from motor disabilities. Deep neural networks consisting of multiple convolutional, LSTM and fullyconnected layers are created to decode EEG signals to maximize the human intention recognition accuracy. However, prior FPGA, ASIC, ReRAM and photonic accelerators cannot maintain sufficient battery lifetime when processing realtime intention recognition. In this paper, we propose an ultralowpower photonic accelerator, MindReading, for human intention recognition by only low bitwidth addition and shift operations. Compared to prior neural network accelerators, to maintain the realtime processing throughput, MindReading reduces the power consumption by 62.7% and improves the throughput per Watt by 168%.

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.