Enhancing Neurorehabilitation through Closed-Loop Control of Robotic Exoskeletons and Brain-Computer Interfaces

Abstract

Neurorehabilitation is an important part of getting better for people who have nerve illnesses or accidents. Brain-computer interfaces (BCIs) and robotic exoskeletons have shown promise in better recovery results by allowing focused therapy and making neurons more flexible. This paper gives an outline of the current study on closed-loop control systems that combine robotic exoskeletons and brain-computer interfaces (BCIs) for neurorehabilitation. It also talks about the pros and cons of these systems. Closed-loop control systems try to make two-way communication possible between the user's brain activity (measured by BCIs) and the robotic suit. This way, treatments can be changed in real time based on the user's neurological signs. This method makes it possible for more personalized and flexible therapy plans, which can help people recover their movement skills and become more useful. When BCIs are combined with artificial exoskeletons, they offer many benefits, such as exact control over movement parameters, increased involvement and motivation through immersive feedback, and the chance to induce learning by coordinating brain activity and physical output. To make these systems work better, though, problems like signal processing delay, calibration issues, and the need for user training must be fixed. Recent research has shown that closed-loop control systems can be used and might be helpful in a number of neurorehabilitation situations, such as helping people who have had a stroke or spinal cord injury get their movement skills back and helping people with neurological conditions regain their independence. In the future, researchers should work on improving these systems' algorithms and hardware, running large-scale clinical studies to show that they work, and finding new ways to improve neurorehabilitation results by combining modern technology.