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. This work was partly supported by the Brain Science and Brain-like Intelligence Technology-National Science and Technology Major Project (2025ZD0218800), the Key R&D Program of Zhejiang (2023C03001), the Zhejiang Provincial Natural Science Foundation of China (LR24F020002), the National Natural Science Foundation of China (62336007), and the Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study (SN-ZJU-SIAS-002).
The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. These authors contributed equally: Xiaomeng Yang, Jie Qin. MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University, Hangzhou, China Xiaomeng Yang, Jie Qin, Xinzhu Xiong & Yu Qi Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China College of Computer Science and Technology, Zhejiang University, Hangzhou, China Xiaomeng Yang, Jie Qin, Xinzhu Xiong, Yueming Wang & Yu Qi Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China State Key Laboratory of Brain-Machine Intelligence, Hangzhou, China The authors declare no competing interests.
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