Neural interfaces are an innovative technology for controlling external devices such as prosthetics and assistive robots. However, they are limited by low bit-rate communication and inaccurate decoders, often leading to misinterpretation of user intentions. Additionally, the current approach of using a single mental task at a time restricts control flexibility, while existing feedback systems are unintuitive and fail to integrate contextual environmental information.
Our device addresses these limitations by enhancing activation pattern generation in brain signals, optimizing neural control of robots, and introducing an advanced, immersive feedback system that is scalable across various applications.