Longitudinal Multimodal Profiling of Balance and Gait In Stroke Using EEG and Lower Limb Sensors: A Feasibility Study.
Longitudinal Multimodal Profiling of Balance and Gait In Stroke Using EEG and Lower Limb Sensors: A Feasibility Study.
Balance and gait recovery is a critical aspect of post-stroke motor rehabilitation. Researchers have effectively utilized EEG to investigate different aspects of lower limb motor control, however there are several technical challenges in the existing brain computer interface (BCI) motor profiling.
The study aims to test the EEG-BCI system to see if it's effective in understanding the balance and walking patterns of post-stroke populations.
Brain Computer Interface represent a groundbreaking field at the crossroads of neuroscience and engineering, serving as a direct communication link between the human brain and computer system.
Despite advancements in BCI technology, the electrocortical oscillations during human walking remain relatively unexplored, providing an opportunity for pioneering investigations.
The research highlights the feasibility of using EEG to decode neural patterns associated with various functions and aims to contribute to existing knowledge by using advanced EEG-based techniques to predict balance and gait patterns with the ultimate goal of tailoring rehabilitation approaches to individual patient needs.
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