Title: GRAPHSENSOR: A Graph Attention Network for Time-Series Sensor Data. Abstract: Our work focuses on the exploration of the internal relationships of signals in an individual sensor. In particular, we address the problem of not being able to evaluate such inter-sensor relationships due to missing rich and explicit feature representation. To solve this problem, we propose GRAPHSENSOR, a graph attention network, with a shared-weight convolution feature encoder to generate the signal segments and learn the internal relationships between them. Furthermore, we enrich the representation of the features by utilizing a multi-head approach when creating the internal relationship graph. Compared with traditional multi-head approaches, we propose a more efficient convolution-based multi-head mechanism, which only requires 56% of model parameters compared with the best multi-head baseline as demonstrated in the experiments. Moreover, GRAPHSENSOR is capable of achieving the state-of-the-art performance in the electroencephalography dataset and improving the accuracy by 13.8% compared to the best baseline in an inertial measurement unit (IMU) dataset.