Decoding Imaginary Handwriting Trajectories of Multi-Stroke Characters for Universal Brain-to-text Translation

The potential of decoding handwriting trajectories from brain signals for use in brain-to-text communication has yet to be fully explored. Here, we developed a novel brain-computer interface (BCI) paradigm that tried to fit the trajectories of imaginary handwriting movements from intracortical motor neural activities and translate them into texts using machine learning approach. The trajectories for handwriting of digits and multi-stroke characters were decoded using a diverse array of neural signals, achieving an average correlation coefficient of 0.75. We developed a speed profile identifier based handwriting recognition algorithm, which accomplished a recognition rate of around 80% within an extensive database of 1000 characters. Additionally, our research uncovered a notable distinction in the neuronal direction tuning between writing strokes and cohesions (air connections between strokes), leveraging which a dual-model approach could exploit to enhance performance by up to 11.7%. Collectively, these findings demonstrated a new approach for BCIs that could possibly implement a universal brain-to-text communication system for any written languages. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was funded by STI 2030-Major Projects (2021ZD0200404), National Natural Science Foundation of China (62336007), Pioneer R&D Program of Zhejiang (2024C03001), the Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study (SN-ZJU-SIAS-002), and the Fundamental Research Funds for the Central Universities (2023ZFJH01-01, 2024ZFJH01-01). The authors thank Mr. Xiang Li for software development, Prof. Schwartz for implantation surgery. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Approved by the Medical Ethics Committee of The Second Affiliated Hospital of Zhejiang University (Ethical review number 2019-158, approved on 05/22/2019), registered in the Chinese Clinical Trial Registry (chictr.org.cn; registration number: ChiCTR2100050705) I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors