dataset-automaticArtifactRemoval

Dataset: EEG recordings containing typical artifacts (EOG, facial/neck EMG, movement)

This repository provides an EEG dataset designed for benchmarking artifact removal and denoising methods.
It contains recordings with typical physiological and movement-related artifacts, including:

• EOG / ocular artifacts (eye blinks, eye movements)
• EMG artifacts (facial and neck muscle activity)
• movement artifacts (motion-related disturbances)

Note: This dataset and its documentation were created as part of an internal research effort and were never published as a paper. The repository is released to support reproducible research and method benchmarking.

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What this dataset is for

The dataset is intended for:

• evaluating automatic EEG artifact removal pipelines
• training / testing ML models for EEG denoising (supervised or self-supervised)
• comparing artifact correction strategies (e.g., regression, ICA-based removal, ASR, deep learning, etc.)
• studying artifact characteristics across modalities

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Repository contents

This repository includes:

• data/
  EEG recordings used for artifact removal evaluation and testing.

• documentation_dataset-automaticArtifactRemoval.pdf
  Primary documentation describing study setup, recording procedure, signal details, and data format.

• event_markers.xlsx
  Marker definitions and coding scheme for events / artifact segments.

• comments_experiment.xlsx
  Notes and comments on recordings / participants / sessions.

• README.md
  Repository-level overview (this file).

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Dataset description (in brief)

Recording intent

The recordings were designed to capture EEG segments with both:

• clean resting-state / baseline-like EEG, and
• controlled or naturally occurring artifacts, including ocular, muscle, and movement artifacts.

Artifact categories

The dataset includes artifacts that commonly impair EEG in real-world experiments:

• ocular activity (EOG)
• facial muscle tension or jaw activity (EMG)
• neck muscle activity (EMG)
• head/body movement artifacts

For the exact artifact taxonomy and marker coding, refer to:

• documentation_dataset-automaticArtifactRemoval.pdf
• event_markers.xlsx