Abstract
Physiological artifacts pose persistent challenges in electroencephalogram (EEG) data acquisition, often compromising interpretation and post-analysis of EEG signals across research and clinical applications. To address such limitations, including various artifact types, insufficient annotations, and low spatial resolutions, we present PhysioMotion Artifact, a large-scale, task-driven EEG dataset with point-wise artifact annotations. EEG data was acquired from 30 healthy participants performing 16 systematically designed single-type and multi-type movement tasks, inducing 14 distinct types of physiological artifacts. To demonstrate the utility of the dataset, we implemented a Convolutional Neural Networks-Transformer hybrid model for artifact detection and classification, achieving 95.4% accuracy in binary classification and 79.7% in 14-class classification tasks.
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Data availability
The dataset is publicly available on OpenNeuro platform (https://doi.org/10.18112/openneuro.ds006386.v1.0.1)30.
Code availability
All code utilized in this study has been made publicly available on GitHub (https://github.com/JiangweiYu221/PhysioMotion_Artifact), encompassing modules for data preprocessing, the annotation interface, the annotation verification tool, and model training procedures. Comprehensive instructions for setup and usage are provided in the accompanying README file included in the repository.
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Acknowledgements
This research was funded by the National Key Research and Development Program of China (Grant No. 2022YFC2405600), the National Natural Science Foundation of China (Grants No. T2225025, 31400842). We also gratefully acknowledge the support provided by the Big Data Computing Center of Southeast University and Visiting Scholar programme at the UGR (Spain).
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C.Y., G.Z., M.C., Y.Z., J.G. and Y.C. developed the conceptual framework for the study. C.Y., Y.Z. and W.X. provided the necessary resources. J.Y. conceived and conducted the experiments with the help of X.W., A.H. constructed the model and analysed the results. The whole process was supervised by C.Y., G.Z., Y.Z., J.G. and M.C. J.Y., A.H. and M.C. contributed to the writing of this manuscript. All authors reviewed the manuscript.
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Yang, C., Yu, J., He, A. et al. PhysioMotion Artifact: A task-driven EEG dataset with point-wise motion artifact annotations. Sci Data (2026). https://doi.org/10.1038/s41597-026-07120-7
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DOI: https://doi.org/10.1038/s41597-026-07120-7
