A paper in Nature reports that a Braingate Brain Computer Interface enabled a man paralyzed from the neck down to have his thoughts translated to text with 94% accuracy. An intracortical BCI decodes attempted handwriting movements from neural activity in the motor cortex and translates it to text in real-time, using a recurrent neural network decoding approach.
The patient paralysed from spinal cord injury, achieved typing speeds of 90 characters per minute with 94.1% raw accuracy online, and greater than 99% accuracy offline with a general-purpose autocorrect.
These typing speeds exceed those reported for any other BCI, and are comparable to typical smartphone typing speeds of individuals in the age group of our participant.
Finally, theoretical considerations explain why complex movements, such as handwriting, may be fundamentally easier to decode than point-to-point movements.
This is a new approach for BCIs and demonstrates the feasibility of accurately decoding rapid movements years after paralysis.
With a focus on practical applications and reliability, the team aims to develop assistive BCI technology to restore independence and communication in individuals with impaired movement abilities.
Previous wireless BCIs have not been able to match the fidelity of their wired counterparts, a limitation that is overcome by BrainGate’s high-bandwidth transmission protocol.