Online EEG Analysis for Neurofeedback in Neurorehabilitation

Stroke remains one of the worlds leading causes of long-term disability, with over 24 million new cases each year. While medical advances have reduced the number of deaths, many stroke survivors continue to face serious challenges in regaining movementespecially in their legs. Rehabilitation is essential, and new technologies are opening exciting possibilities. This research project is exploring how brain-computer interfaces (BCIs) can be used to support recovery after stroke. BCIs use brain signals, measured through a safe and non-invasive method called electroencephalography (EEG), to detect when a person is imagining movement. This information can then be used to trigger real-time feedback or activate robotic assistance. While BCIs have already shown promise in helping people regain arm and hand function, their use in leg rehabilitation has been more limited. This project aims to develop advanced AI-based algorithms, which improve the systems ability to detect imagined leg movements from brain signals. These signals will be linked to a state-of-the- art robotic device that can guide the legs through natural movement patterns and deliver feedback to the user in real time. The goal is to create a fully interactive closed-loop rehabilitation system, where patients can mentally engage in movement, see and feel the results, and strengthen the brain-body connection critical to recovery. This innovative approach could lead to more effective therapies for restoring mobility and independence after strokeand take a major step forward in the future of neurorehabilitation.