Data availability

The EEG dataset generated and analyzed in this study is publicly available on figshare (https://doi.org/10.6084/m9.figshare.29987758.v4). The data are structured in compliance with the EEG-BIDS standard, featuring participant- and session-level directories alongside detailed metadata (e.g., data description, participant information, electrode locations, and event markers) to support reproducible research. Additional resources, including experimental stimuli and custom code for data preprocessing, model training, and visualization, are available within the same repository.

Code availability

All code scripts used in this study are publicly available in the same figshare repository as the dataset (https://doi.org/10.6084/m9.figshare.29987758.v4), under the code/ directory. Users can locate the code by scrolling through the online file list or downloading the full repository and navigating to /code. Detailed setup instructions—including mappings to the original BIDS format—are provided in code/README.md.

References

Feigin, V. L. et al. Global, regional, and national burden of neurological disorders, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 18, 459–480, https://doi.org/10.1016/s1474-4422(18)30499-x (2019).

Google Scholar 1.

Claassen, J. et al. Detection of brain activation in unresponsive patients with acute brain injury. N. Engl. J. Med. 380(26), 2497–2505, https://doi.org/10.1056/NEJMoa1812757 (2019).

Google Scholar 1.

Pels, E. G. M. et al. Estimated prevalence of the target population for brain-computer interface neurotechnology in the Netherlands. Neurorehabil. Neural Repair 31(7), 677–685, https://doi.org/10.1177/1545968317714577 (2017).

Google Scholar 1.

Willett, F. R. et al. High-performance brain-to-text communication via handwriting. Nature 593(7858), 249–254, https://doi.org/10.1038/s41586-021-03506-2 (2021).

Google Scholar 1.

Metzger, S. L. et al. Generalizable spelling using a speech neuroprosthesis in an individual with severe limb and vocal paralysis. Nat. Commun. 13(1), 6510, https://doi.org/10.1038/s41467-022-33611-3 (2022).

Google Scholar 1.

Moses, D. A. et al. Neuroprosthesis for decoding speech in a paralyzed person with anarthria. N. Engl. J. Med. 385(3), 217–227, https://doi.org/10.1056/nejmoa2027540 (2021).

Google Scholar 1.

Hochberg, L. R. et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485(7398), 372–375, https://doi.org/10.1038/nature11076 (2012).

Google Scholar 1.

Willett, F. R. et al. A high-performance speech neuroprosthesis. Nature 620(7976), 1031–1036, https://doi.org/10.1038/s41586-023-06377-x (2023).

Google Scholar 1.

Metzger, S. L. et al. A high-performance neuroprosthesis for speech decoding and avatar control. Nature 620(7976), 1037–1046, https://doi.org/10.1038/s41586-023-06443-4 (2023).

Google Scholar 1.

Défossez, A. et al. Decoding speech perception from non-invasive brain recordings. Nat. Mach. Intell. 5(10), 1097–1107, https://doi.org/10.1038/s42256-023-00714-5 (2023).

Google Scholar 1.

Yang, X. et al. Reconstructing multi-stroke characters from brain signals toward generalizable handwriting brain-computer interfaces. IEEE Trans. Neural Syst. Rehabil. Eng. 32, 4230–4239, https://doi.org/10.1109/TNSRE.2024.3492191 (2024).

Google Scholar 1.

Qi, Y. et al. Human motor cortex encodes complex handwriting through a sequence of stable neural states. Nat. Hum. Behav. 9, 1260–1271, https://doi.org/10.1038/s41562-025-02157-x (2025).

Google Scholar 1.

Aflalo, T. et al. Decoding motor imagery from the posterior parietal cortex of a tetraplegic human. Science 348, 906–910, https://doi.org/10.1126/science.aaa5417 (2015).

Google Scholar 1.

Bouton, C. E. et al. Restoring cortical control of functional movement in a human with quadriplegia. Nature 533, 247–250, https://doi.org/10.1038/nature17435 (2016).

Google Scholar 1.

Jarosiewicz, B. et al. Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface. Sci. Transl. Med. 7(313), 313ra179–313ra179, https://doi.org/10.1126/scitranslmed.aac7328 (2015).

Google Scholar 1.

Chen, X. et al. High-speed spelling with a noninvasive brain–computer interface. Proc. Natl. Acad. Sci. USA. 112(44), E6058–E6067, https://doi.org/10.1073/pnas.1508080112 (2015).

Google Scholar 1.

Boto, E. et al. Moving magnetoencephalography towards real-world applications with a wearable system. Nature 555(7698), 657–661, https://doi.org/10.1038/nature26147 (2018).

Google Scholar 1.

Abiri, R. et al. A comprehensive review of EEG-based brain–computer interface paradigms. J. Neural Eng. 16(1), 011001, https://doi.org/10.1088/1741-2552/aaf12e (2019).

Google Scholar 1.

Rashid, M. et al. Current status, challenges, and possible solutions of EEG-based brain-computer interface: a comprehensive review. Front. Neurorobot. 14, 25, https://doi.org/10.3389/fnbot.2020.00025 (2020).

Google Scholar 1.

He, H. & Wu, D. Transfer learning for brain–computer interfaces: A Euclidean space data alignment approach. IEEE Trans. Biomed. Eng. 67(2), 399–410, https://doi.org/10.1109/TBME.2019.2913914 (2019).

Google Scholar 1.

Dai, B. et al. A reliability-enhanced Brain–Computer Interface via Mixture-of-Graphs-driven Information Fusion. Inform. Fusion 120, 103069, https://doi.org/10.1016/j.inffus.2025.103069 (2025).

Google Scholar 1.

Yang, B. et al. A multi-day and high-quality EEG dataset for motor imagery brain-computer interface. Sci. Data 12(1), 488, https://doi.org/10.1038/s41597-025-04826-y (2025).

Google Scholar 1.

Liu, Y. et al. Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients. Sci. Data 12(1), 314, https://doi.org/10.1038/s41597-025-04618-4 (2025).

Google Scholar 1.

Ma, J. et al. A large EEG dataset for studying cross-session variability in motor imagery brain-computer interface. Sci. Data 9(1), 531, https://doi.org/10.1038/s41597-022-01647-1 (2022).

Google Scholar 1.

Liu, B. et al. eldBETA: A large eldercare-oriented benchmark database of SSVEP-BCI for the aging population. Sci. Data 9(1), 252, https://doi.org/10.1038/s41597-022-01372-9 (2022).

Google Scholar 1.

Won, K. et al. EEG dataset for RSVP and P300 speller brain-computer interfaces. Sci. Data 9(1), 388, https://doi.org/10.1038/s41597-022-01509-w (2022).

Google Scholar 1.

Wang, F. et al. figshare, https://doi.org/10.6084/m9.figshare.29987758.v4 (2025). 1.

Pernet, C. R. et al. EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Sci. Data 6(1), 103, https://doi.org/10.1038/s41597-019-0104-8 (2019).

Google Scholar 1.

Lawhern, V. J. et al. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. J. Neural Eng. 15, 056013, https://doi.org/10.1088/1741-2552/aace8c (2018).

Google Scholar

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant Nos. 62376112, 82172058, 81771926, 61763022, 62366026, and 62006246) and the China Postdoctoral Science Foundation (Grant No. 2023M734315).

Author information

Authors and Affiliations

Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China

Fan Wang, Yanxiao Chen, Peng Wang, Jiaping Xu & Yunfa Fu 1.

Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, 650500, China

Fan Wang, Yanxiao Chen, Peng Wang & Yunfa Fu 1.

School of Information Engineering, Chinese People’s Armed Police Force Engineering University, Xi’an, 710000, China

Anmin Gong

Authors

1. Fan Wang
2. Yanxiao Chen
3. Peng Wang
4. Anmin Gong
5. Jiaping Xu
6. Yunfa Fu

Contributions

F.W. conceived the study, designed the experiments, analyzed the data, and wrote the original manuscript. Y.C. and P.W. conducted the experiments and performed data collection. A.G. assisted in securing partial funding support and, together with J.X., provided technical support and data interpretation. Y.F. supervised the project, acquired funding, and critically revised the manuscript. All authors reviewed and approved the final manuscript.

Corresponding author

Correspondence to Yunfa Fu.

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The authors declare no competing interests.

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Wang, F., Chen, Y., Wang, P. et al. An EEG dataset for handwriting imagery decoding of Chinese character strokes and Pinyin single vowels. Sci Data (2026). https://doi.org/10.1038/s41597-026-06708-3

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Received: 04 September 2025

Accepted: 23 January 2026

Published: 02 February 2026

DOI: https://doi.org/10.1038/s41597-026-06708-3