Abstract
Non-invasive EEG-based brain-computer interfaces (BCI) for handwriting imagery can support the restoration of fine writing abilities in individuals with motor impairments. However, the development of high-performance decoding algorithms is constrained by scarce training datasets. To address this, we present the first open EEG dataset dedicated to handwriting imagery. The dataset comprises 32-channel EEG recordings (sampled at 1000 Hz) from 21 healthy participants across two sessions separated by at least 24 hours. A dual-paradigm design captures multidimensional neural features: a Chinese character stroke imagery task (five basic strokes, 200 trials per session) and a Pinyin single-vowel imagery task (six vowels, 240 trials per session). After rigorous quality screening, 18,480 standardized trials are provided, ensuring completeness, reliability, and adherence to the Brain Imaging Data Structure (BIDS) standard. This dataset enables the development and evaluation of algorithms for non-invasive BCI and supports research on restoring writing-based communication in individuals with motor impairments.
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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.
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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).
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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.
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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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DOI: https://doi.org/10.1038/s41597-026-06708-3
