Meeting brain–computer interface user performance expectations using a deep neural network decoding framework

Brain-computer interface (BCI) neurotechnology has the potential to reduce disability associated with paralysis by translating neural activity into control of assistive devices1-9. Surveys of potential end-users have identified key BCI system features10-14, including high accuracy, minimal daily setup, rapid response times, and multifunctionality. These performance characteristics are primarily influenced by the BCI's neural decoding...