Evidence map›Paper›PMID 41957382›Full record

ArticleScientific data2026

PhysioMotion Artifact: A task-driven EEG dataset with point-wise motion artifact annotations.

Chunfeng Yang, Jiangwei Yu, Aonan He, Wentao Xiang, Xi Wang, Guangquan Zhou, Yudong Zhang, Miao Cao, Yang Chen, Juan M Gorriz

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PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Chunfeng Yang *Key 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.
Jiangwei Yu *School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096, China. yjiangwei210@163.com.
Aonan HeKey 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.
Wentao XiangJiangsu Province Engineering Research Center for Smart Wearable and Rehabilitation Devices, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166, Jiangsu, China.
Xi WangKey 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.
Guangquan ZhouSchool of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096, China. guangquan.zhou@seu.edu.cn.
Yudong ZhangKey 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.
Miao CaoSchool of Health Sciences, Swinburne University of Technology, John Street, Hawthorn, Victoria, 3122, Australia. miaocao@swin.edu.au.
Yang ChenKey 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.
Juan M GorrizData Science and Computational Intelligence Institute, University of Granada, Granada, 52005, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

ArtifactsElectroencephalographyConvolutional Neural NetworksHumansMovement

Identifiers

PMID41957382
PMCPMC13223301

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.