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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.

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Summary

This summary is machine-generated.

This study introduces PhysioMotion Artifact, a large electroencephalogram (EEG) dataset for improving artifact detection. The dataset aids in classifying 14 physiological artifact types from movement tasks.

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Area of Science:

Neuroscience
Biomedical Engineering
Data Science

Background:

Physiological artifacts in electroencephalogram (EEG) data present significant challenges for accurate signal interpretation in research and clinical settings.
Existing limitations include diverse artifact types, inadequate annotations, and low spatial resolution, hindering reliable EEG analysis.

Purpose of the Study:

To introduce PhysioMotion Artifact, a novel, large-scale, task-driven EEG dataset with precise, point-wise annotations for physiological artifacts.
To facilitate improved artifact detection and classification in EEG signals.

Main Methods:

Acquired EEG data from 30 healthy participants engaged in 16 distinct single-type and multi-type movement tasks.
Induced and annotated 14 different types of physiological artifacts within the EEG data.
Developed and evaluated a hybrid Convolutional Neural Networks-Transformer model for artifact detection and classification.

Main Results:

The PhysioMotion Artifact dataset provides comprehensive, point-wise annotations for 14 physiological artifact types.
The hybrid CNN-Transformer model achieved 95.4% accuracy for binary artifact classification.
The model attained 79.7% accuracy for classifying 14 distinct artifact types.

Conclusions:

The PhysioMotion Artifact dataset is a valuable resource for advancing research in EEG artifact removal and analysis.
The demonstrated model performance highlights the dataset's utility in developing robust artifact detection and classification systems.