High-performance brain-to-text communication via imagined handwriting
Brain-computer interfaces (BCIs) can restore communication to people who have lost the ability to move or speak.
However, rapid sequences of highly dexterous behaviors, such as handwriting or touch typing, might enable faster communication rates.
Here, we demonstrate an intracortical BCI that can decode imagined handwriting movements from neural activity in motor cortex and translate it to text in real-time, using a novel recurrent neural network decoding approach.
With this BCI, our study participant (whose hand was paralyzed) achieved typing speeds that exceed those of any other BCI yet reported: 90 characters per minute at >99% accuracy with a general-purpose autocorrect.
These speeds are comparable to able-bodied smartphone typing speeds in our participant’s age group (115 characters per minute)8 and significantly close the gap between BCI-enabled typing and able-bodied typing rates.
Finally, new 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.