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bioRxiv · 10.1101/2025.10.10.681755

PhysioMotion Artifact: A Task-Driven EEG Dataset with Channel-Level Motion Artifact Annotations

Yang, C.·Yu, J.·He, A.·Xiang, W.·Wang, X.·Zhou, G.·Zhang, Y.·Cao, M.·Chen, Y.·Gorriz, J. M.
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Abstract

Physiological artifacts pose persistent challenges in electroencephalo-gram (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 channel-level 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 98% accuracy in binary classification and 85% in 14-class classification tasks.

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Yang, C., Yu, J., He, A., Xiang, W., Wang, X., Zhou, G., Zhang, Y., Cao, M., Chen, Y., Gorriz, J. M.. 2025-10-13. PhysioMotion Artifact: A Task-Driven EEG Dataset with Channel-Level Motion Artifact Annotations. https://doi.org/10.1101/2025.10.10.681755

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