Cheng Xuan

Ard Kastrati, Josua Bürki, Jonas Lauer et al.

We introduce a unified benchmarking framework focused on evaluating EEG-based foundation models in clinical applications. The benchmark spans 11 well-defined diagnostic tasks across 14 publicly available EEG datasets, including epilepsy, schizophrenia, Parkinson's disease, OCD, and mild traumatic brain injury. It features minimal preprocessing, standardized evaluation protocols, and enables side-by-side comparisons of classical baselines and modern foundation models. Our results show that while foundation models achieve strong performance in certain settings, simpler models often remain competitive, particularly under clinical distribution shifts. To facilitate reproducibility and adoption, we release all prepared data and code in an accessible and extensible format.

Cheng Xuan, Zhiqiang Tang, Jinxin Xu

One of the most efficient ways for a learning-based robotic arm to learn to process complex tasks as human, is to directly learn from observing how human complete those tasks, and then imitate. Our idea is based on success of Deep Q-Learning (DQN) algorithm according to reinforcement learning, and then extend to Deep Deterministic Policy Gradient (DDPG) algorithm. We developed a learning-based method, combining modified DDPG and visual imitation network. Our approach acquires frames only from a monocular camera, and no need to either construct a 3D environment or generate actual points. The result we expected during training, was that robot would be able to move as almost the same as how human hands did.