Brain–computer interfaces (BCIs) can convert brain signals into instructions for external devices, which offers a potential route to restore communication for people with paralysis. For years, BCIs had advanced incrementally, with intracortical point-and-click typing plateauing at roughly 40 correct characters per minute — functional in principle, but far from natural communication. Then, in 2021, Willett and colleagues reported a BCI that decoded attempted handwriting from motor cortex activity in real time, achieving surprisingly high typing speeds of 90 characters per minute with 94% accuracy.
Indeed, this BCI had approached the typing speed of able-bodied smartphone users, and, for many in the field, it was the moment when practical, high-performance neural communication truly felt within reach. What made the work feel so elegant, beyond its striking performance, was the core insight behind its success. Rather than asking users to produce simple point-to-point reaching movements, the team leaned into handwriting, a motor skill honed over decades of practice.
They recorded neural activity from the motor cortex while a participant with tetraplegia attempted to handwrite letters and symbols, and used a recurrent neural network to classify the characters from neural signals in real time. They demonstrated rigorously that handwriting carries far higher temporal dimensionality than point-to-point reaching movements, and that this richer spatiotemporal structure widens the neural distances between characters, which makes them inherently more robust to noise and easier to distinguish. It was a beautiful reminder that the brain’s own well-trained motor programmes could be an ideal substrate for BCIs.
It shifted the field’s perspective on what kinds of movements to decode. The approach has since inspired a wave of research exploring other complex sequential behaviours, from speech decoding to internal thought translation. This is a preview of subscription content, access via your institution Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription Receive 12 print issues and online access Prices may be subject to local taxes which are calculated during checkout Willett, F.
High-performance brain to text communication via handwriting. Nature 593, 249–254 (2021) Article CAS PubMed PubMed Central Google Scholar Qi, Y. et al. Human motor cortex encodes complex handwriting through a sequence of stable neural states.
Behav. 9, 1260–1271 (2025) Article PubMed PubMed Central Google Scholar MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou, China The author declares no competing interests. High-performance handwriting brain–computer interfaces. Neurosci. (2026). https://doi.org/10.1038/s41583-026-01082-w DOI: https://doi.org/10.1038/s41583-026-01082-w
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