Brain-writing-BERT for generalizable handwriting brain-computer interface

Handwriting Brain-Computer Interfaces (BCIs) offer a direct brain-to-text pathway, holding significant promise for neural rehabilitation. However, existing research has primarily focused on alphabetic languages. Unlike the sequential nature of alphabetic handwriting, logographic characters like Chinese incorporate complex spatial structures that embed semantic information, presenting a greater challenge for neural decoding. To address this, we propose Brain-Writing-BERT, a novel BCI framework that learns the structural semantic priors from logographic characters to facilitate decoding. We evaluate our system with a clinical participant in Chinese character decoding tasks. All clinical and experimental procedures were registered in the Chinese Clinical Trial Registry (ref.ChiCTR2100050705, date of registration: 03/09/2021, registry name: Research on Brain-Computer Interfaces for Individuals with High Cervical Spinal Paraplegia). The proposed BCI achieves an online accuracy of 94.45% for a 295-character vocabulary, with an average speed of 8.55 ± 2.77 characters per minute. This study advances high-performance handwriting BCIs for logographic systems, broadening communication possibilities for diverse languages.