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Updated: Jun 12, 2026

Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI
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Supervised autoencoder for gradient and BCG artifact removal in EEG during simultaneous EEG-fMRI.

K A Shahriar1, E H Bhuiyan2, Qingfei Luo3

  • 1Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh.

Magnetic Resonance Imaging
|June 10, 2026
PubMed
Summary

Deep Artifact Removal (DAR) effectively suppresses MRI artifacts in simultaneous EEG-fMRI recordings. This AI method significantly improves signal quality while preserving crucial brain activity, outperforming existing techniques.

Keywords:
Artifact removalBallistocardiogramConvolutional autoencoderDeep learningEEG-fMRIGradient artifact

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Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Area of Science:

  • Neuroimaging
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) offer complementary insights into brain function.
  • Magnetic resonance imaging (MRI) introduces gradient (GA) and ballistocardiogram (BCG) artifacts that contaminate EEG signals, hindering analysis.
  • Existing artifact removal methods often struggle to balance artifact suppression with physiological signal preservation.

Purpose of the Study:

  • To introduce Deep Artifact Removal (DAR), a novel deep learning approach for suppressing MRI-induced artifacts in simultaneous EEG-fMRI.
  • To evaluate DAR's effectiveness in artifact reduction and its ability to preserve physiological neural activity.
  • To compare DAR's performance against established artifact removal techniques.

Main Methods:

  • Developed DAR, a supervised 1D convolutional autoencoder.
  • Trained DAR on the public Carbon Wire Loop EEG-fMRI dataset using paired artifact-contaminated and MR-corrected data segments.
  • Validated DAR's performance using metrics such as Root Mean Square Error (RMSE) and Structural Similarity Index Measure (SSIM), and assessed signal-to-noise ratio (SNR) gain.
  • Employed leave-one-subject-out cross-validation to test generalization to unseen subjects.
  • Utilized saliency analysis to identify influential patterns for artifact reduction.

Main Results:

  • DAR achieved excellent artifact suppression, with low RMSE (0.022 ± 0.015) and high SSIM (0.888 ± 0.091) across subjects.
  • Demonstrated significant SNR gain (14.63 dB, p < 0.001) with DAR.
  • Showcased strong generalization capabilities, maintaining good performance on unseen subjects (RMSE 0.063 ± 0.011, SSIM 0.666 ± 0.088).
  • Preserved physiological fidelity by retaining over 75% of occipital alpha power (8-12 Hz).
  • Outperformed PCA, ICA, average artifact subtraction, and wavelet denoising in key performance metrics.

Conclusions:

  • Deep Artifact Removal (DAR) is a highly effective deep learning method for mitigating MRI artifacts in simultaneous EEG-fMRI.
  • DAR significantly enhances EEG signal quality without compromising neurophysiologically relevant brain activity.
  • DAR represents a superior alternative to conventional methods for artifact removal in combined EEG-fMRI studies.