News Finding: Byron M. Yu, assistant professor of electrical and computer engineering and biomedical engineering at Carnegie Mellon, believes this work demonstrates the utility of BCI for basic scientific studies that will eventually impact people's lives.
Paper Finding: These results show that the intrinsic manifold was a reliable predictor of the learnability of a BCI mapping: new BCI mappings that were within the intrinsic manifold were more learnable than those outside of it.
News Context: Because the existing brain patterns likely reflect how the neurons are interconnected, the results suggest that the connectivity among neurons shapes learning. Byron M. Yu, assistant professor of electrical and computer engineering and biomedical engineering at Carnegie Mellon, believes this work demonstrates the utility of BCI for basic scientific studies that will eventually impact people's lives. "These findings could be the basis for novel rehabilitation procedures for the many neural disorders that are characterized by improper neural activity," Yu says.
Paper Context: 4) . These results show that the intrinsic manifold was a reliable predictor of the learnability of a BCI mapping: new BCI mappings that were within the intrinsic manifold were more learnable than those outside of it. We considered five alternative explanations for the difference in learnability.