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Related Experiment Video

Updated: Aug 2, 2025

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
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The MotoNet: A 3 Tesla MRI-Conditional EEG Net with Embedded Motion Sensors.

Joshua Levitt1, André van der Kouwe2, Hongbae Jeong2

  • 1Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.

Sensors (Basel, Switzerland)
|April 13, 2023
PubMed
Summary
This summary is machine-generated.

A new electroencephalogram (EEG) net, MotoNet, integrates motion sensors to improve MRI scans by reducing motion artifacts and ballistocardiogram (BCG) noise, benefiting epilepsy monitoring in children.

Keywords:
EEG/fMRIKalman adaptive noise cancellationballistocardiogramposition estimation

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Medical Imaging

Background:

  • Motion during Magnetic Resonance Imaging (MRI) significantly limits scan quality, particularly for pediatric epilepsy patients.
  • Electroencephalogram (EEG) signals are highly susceptible to motion-induced noise, including ballistocardiogram (BCG) artifacts from heartbeats.

Purpose of the Study:

  • To introduce MotoNet, an innovative EEG net designed to simultaneously monitor EEG and track head motion during MRI.
  • To assess the safety, efficacy, and artifact reduction capabilities of the MotoNet system.

Main Methods:

  • The MotoNet utilizes polymer thick film (PTF) EEG leads and integrated motion sensors within a flex circuit.
  • EEG and motion data were acquired using a commercial 3 Tesla (T) MRI system.
  • A Kalman filtering algorithm was employed for BCG noise correction.

Main Results:

  • MRI safety tests confirmed minimal heating (below 1 °C) at 3 T.
  • Head position correlated linearly with motion sensor output when using specific MRI sequences.
  • Kalman filtering effectively reduced BCG noise, yielding artifact-clean EEG signals.

Conclusions:

  • MotoNet integrates 32 EEG electrodes with 32 motion sensors to enhance both EEG and MRI signal quality.
  • The system shows potential for determining net position with custom MRI gradients.
  • Integrated motion sensors aid in reducing BCG noise, improving overall data integrity.