Decoding Handwriting Trajectories from Intracortical Brain Signals for Brain-to-Text Communication.

ABSTRACT: The potential to decode handwriting trajectories from brain signals has yet to be fully explored in clinical brain-computer interfaces (BCIs). Here, intracortical neural signals are recorded from a paralyzed individual during attempted handwriting of complex characters. An innovative decoding framework is introduced to address both shape and temporal distortions between neural activity and movement, effectively resolving the misalignment issue commonly encountered in clinical BCIs due to the lack of accurate movement labels. The results demonstrated the reconstruction of highly accurate and human-recognizable handwriting trajectories, significantly outperforming conventional methods. Furthermore, the new framework enabled effective multi-day data fusion, leading to additional improvements

SUBMITTER: Xu G 

PROVIDER: S-EPMC12561361 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature