PhysioMotion Artifact: A task-driven EEG dataset with point-wise motion artifact annotations
Chunfeng Yang1, Jiangwei Yu2, Aonan He3
1Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, School of Computer Science and Engineering, Southeast University and Centre de Recherche en Information Biomédicale Sino-français (CRIBs), 2 Sipailou, Nanjing, 210096, Jiangsu, China. chunfeng.yang@seu.edu.cn.
None:
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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