A new approach to brain-computer interface has allowed a paralyzed person to type with unprecedented speed, The Scientist reported yesterday. As Science first reported in 2019, researchers used electrodes implanted in a motor region of the brain to read out letters as a person paralyzed from the neck down imagined writing by hand.
Previous systems, where users select on-screen letters by moving a cursor with their minds, have reached speeds up to about 40 characters per minute, but the new approach allowed speeds of up to 90 characters per minute with 94% accuracy, the researchers reported this week in Nature. Future improvements to the setup—such as making it smaller, wireless, and easier to calibrate—could ready it for wider clinical use.
SUMMARY OF THE STUDY
Brain–computer interfaces (BCIs) can restore communication to people who have lost the ability to move or speak. So far, a major focus of BCI research has been on restoring gross motor skills, such as reaching and grasping1,2,3,4,5 or point-and-click typing with a computer cursor6,7.
However, rapid sequences of highly dexterous behaviours, such as handwriting or touch typing, might enable faster rates of communication. Here we developed an intracortical BCI that 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.
With this BCI, our study participant, whose hand was 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.
To our knowledge, 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 (115 characters per minute)8. Finally, theoretical considerations explain why temporally complex movements, such as handwriting, may be fundamentally easier to decode than point-to-point movements.
Our results open a new approach for BCIs and demonstrate the feasibility of accurately decoding rapid, dexterous movements years after paralysis.