Carnegie Mellon Engineers Align Human Neuroplasticity With Machine Learning for Better BCIs

According to reporting from TechXplore Engineering, researchers led by Bin He have developed a sensory-guided framework that allows untrained users to control brain-computer interfaces with high precision. By synchronizing human trial-and-error learning with machine-learning algo

According to reporting from TechXplore Engineering, researchers led by Bin He have developed a sensory-guided framework that allows untrained users to control brain-computer interfaces with high precision. By synchronizing human trial-and-error learning with machine-learning algo