Asset Details
Do you wish to reserve the book?
Cortical representation of multidimensional handwriting movement and implications for neuroprostheses
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Cortical representation of multidimensional handwriting movement and implications for neuroprostheses
Do you wish to request the book?
Cortical representation of multidimensional handwriting movement and implications for neuroprostheses
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Cortical representation of multidimensional handwriting movement and implications for neuroprostheses
2026
Overview
Handwriting brain-computer interfaces (BCIs) have enabled high performance brain-to-text communication for paralyzed individuals. However, the detailed parameters of handwriting movement and their cortical representations remain incompletely understood. Here, we recorded intracortical neural activity from a paralyzed subject and found distinct neural representations for strokes and pen lifts with respect to two-dimensional (2D) velocity on the writing plane, indicating that 2D kinematics alone cannot fully account for the observed neural variance. To address this, we acquired multidimensional handwriting data from healthy subjects, including 3D velocity, grip force, writing pressure, and multi-channel electromyographic (EMG) signals. Incorporating these additional dimensions beyond 2D velocity significantly improved the interpretability of neural signals for both strokes and pen lifts. We further leveraged these additional dimensions to enhance handwriting decoding performance. Together, our findings indicate the motor cortex encodes handwriting as multidimensional movement and highlight the importance of multidimensional features for improving the performance of handwriting BCIs.
This study reveals that the brain encodes handwriting in multiple dimensions. The decoding paradigm, informed by movement data from healthy individuals, captures these full dimensions to enhance the performance of brain-to-text communication.
