Movement intention recognition paves the path to developing Brain-Computer Interface (BCI) applications. Current research mostly focuses on questions like which hand is intended to move. While answering questions like whether a hand is intended to move is more desirable for widely realworld applications, because we cannot continuously perform intention tasks during usage, and the gap periods may cause unintended operation resulting in system failures. However, this kind of intention detection task is more diffi-cult, since for a whether problem, it is hard to know what the not situation is and con-sequently to acquire training samples for the not situation. Furthermore, the occurrence of genuine intentions is comparatively scarce and unexpected, making the intention detec-tion task hard and computation-consuming. To tackle this problem, we propose a Reconstruc-tion-based Intention Detection (RID) frame-work, which utilises a reconstruction model to represent a high-level abstraction of EEG signals and leverages the reconstruction errors to determine whether there is a movement intention. Our framework is not only theoret-ically flexible and robust to any sophisticated real-world scenarios but also hand-crafted feature and domain knowledge free. Compre-hensive experiments on detecting movement intention tasks with different reconstruction models demonstrate the promising perfor-mance of the proposed reconstruction inten-tion detection framework.