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Related Experiment Video

Updated: Apr 10, 2026

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A lightweight deep convolutional neural network for detecting artifacts in continuous EEG signals.

Evans Nyanney1, Parthasarathy D Thirumala2, Shyam Visweswaran3

  • 1Ohio University, Department of Industrial and Systems Engineering, 1 Ohio University Drive, 276 Stocker Center, Athens, 45701, OH, USA.

Clinical Neurophysiology Practice
|April 9, 2026
PubMed
Summary

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Lightweight convolutional neural networks (CNNs) reliably detect EEG artifacts, outperforming traditional methods. Tailored temporal windows optimize performance for eye movement, muscle, and non-physiological artifact types.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Electroencephalography (EEG) is crucial for neurological diagnostics.
  • Artifacts like eye movements, muscle activity, and non-physiological signals can compromise EEG data quality.
  • Automated artifact detection is essential for reliable EEG analysis.

Purpose of the Study:

  • To develop and validate lightweight, artifact-specific convolutional neural networks (CNNs) for automated detection of EEG artifacts.
  • To determine optimal temporal windows for classifying different artifact types (eye movement, muscle, non-physiological).

Main Methods:

  • Trained three binary CNN detectors on the Temple University Hospital EEG artifact corpus using patient-level data splits.
  • Standardized EEG signals to 250 Hz with a 22-channel bipolar montage.
Keywords:
Artifact detectionClinical neurophysiologyContinuous EEGConvolutional neural networkDeep learningSignal processing

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  • Evaluated non-overlapping signal segments ranging from 1 to 30 seconds, optimizing operating points using Youden's J statistic.

    • Main Results:

    • CNNs demonstrated superior performance compared to rule-based detection methods.
    • Optimal detection windows varied by artifact class: 20s for eye movements (ROC AUC 0.975), 5s for muscle artifacts (accuracy 93.2%), and 1s for non-physiological artifacts (F1 0.774).

    Conclusions:

    • Artifact-specific CNNs with class-tailored temporal windows offer a reliable approach to EEG artifact detection.
    • This method surpasses traditional rule-based performance, providing practical guidance for clinical EEG quality control.