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

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.