Uploaded October 2025 | Updated September 2026, 1 day ago
An intuitive, bimanual, high-throughput QWERTY touch typing
neuroprosthesis for people with tetraplegia
This team developed an intracortical BCI typing neuroprosthesis for bimanual QWERTY use by people with paralysis. It decodes attempted finger movements in real-time with minimal calibration and uses a 5-gram language model to improve accuracy. In trials with two participants (ALS and spinal cord injury), typing reached 22 wpm with 1.6% error—near able-bodied performance and faster than current hand motor iBCIs. This approach provides an intuitive, familiar, and easy-to-learn communication method for individuals with impaired motor function.
This team won the 2nd place of the BCI Award 2025. Learn more: bci-award.com/2025
Justin J. Jude 1,2,6, Hadar Levi-Aharoni 1,2,6, Alexander J. Acosta 1, Shane B. Allcroft 6,8, Claire Nicolas 1,6, Bayardo E. Lacayo 1, Nicholas S. Card 9, Maitreyee Wairagkar 9, Alisa D. Levin 11,12, David M. Brandman 9, Sergey D. Stavisky 9, Francis R. Willett 10, Ziv M. Williams 3,4,5, John D. Simeral 8,6,7, Leigh R. Hochberg 8,6,1,2,7, Daniel B. Rubin 1,2
1 Ctr. for Neurotechnology and Neurorecovery, Dept. of Neurology, Massachusetts General Hospital, Boston, MA
2 Dept. of Neurology, Harvard Medical School, Boston, MA
3 Dept. of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA
4 Harvard-MIT Division of Health Sciences and Technology, Boston, MA
5 Harvard Medical School, Program in Neuroscience, Boston, MA
6 Sch. of Engineering, Brown University, Providence, RI
7 Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI
8 VA Ctr. for Neurorestoration and Neurotechnology, VA Providence Healthcare System, Providence, RI
9 Dept. of Neurological Surgery, University of California Davis, Davis, CA
10 Dept. of Neurosurgery, Stanford University, Stanford, CA
11 Department of Computer Science, Stanford University, Stanford, CA
An intuitive, bimanual, high-throughput QWERTY touch typing
neuroprosthesis for people with tetraplegia
This team developed an intracortical BCI typing neuroprosthesis for bimanual QWERTY use by people with paralysis. It decodes attempted finger movements in real-time with minimal calibration and uses a 5-gram language model to improve accuracy. In trials with two participants (ALS and spinal cord injury), typing reached 22 wpm with 1.6% error—near able-bodied performance and faster than current hand motor iBCIs. This approach provides an intuitive, familiar, and easy-to-learn communication method for individuals with impaired motor function.
This team won the 2nd place of the BCI Award 2025. Learn more: bci-award.com/2025
Justin J. Jude 1,2,6, Hadar Levi-Aharoni 1,2,6, Alexander J. Acosta 1, Shane B. Allcroft 6,8, Claire Nicolas 1,6, Bayardo E. Lacayo 1, Nicholas S. Card 9, Maitreyee Wairagkar 9, Alisa D. Levin 11,12, David M. Brandman 9, Sergey D. Stavisky 9, Francis R. Willett 10, Ziv M. Williams 3,4,5, John D. Simeral 8,6,7, Leigh R. Hochberg 8,6,1,2,7, Daniel B. Rubin 1,2
1 Ctr. for Neurotechnology and Neurorecovery, Dept. of Neurology, Massachusetts General Hospital, Boston, MA
2 Dept. of Neurology, Harvard Medical School, Boston, MA
3 Dept. of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA
4 Harvard-MIT Division of Health Sciences and Technology, Boston, MA
5 Harvard Medical School, Program in Neuroscience, Boston, MA
6 Sch. of Engineering, Brown University, Providence, RI
7 Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI
8 VA Ctr. for Neurorestoration and Neurotechnology, VA Providence Healthcare System, Providence, RI
9 Dept. of Neurological Surgery, University of California Davis, Davis, CA
10 Dept. of Neurosurgery, Stanford University, Stanford, CA
11 Department of Computer Science, Stanford University, Stanford, CA
