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EEG-to-EEG: Scalp-to-Intracranial EEG Translation Using a Combination of Variational Autoencoder and Generative

Bahman Abdi-Sargezeh1, Sepehr Shirani2, Antonio Valentin2

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Summary

This study introduces a novel VAE-cGAN model for electroencephalography (EEG) translation, enhancing low-resolution scalp EEG (scEEG) to high-resolution intracranial EEG (iEEG). This improves the detection of epileptic discharges.

Keywords:
IED detectiongenerative adversarial networksinterictal epileptiform dischargescalp-to-intracranial EEG translationvariational autoencoder

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Neuroscience

Background:

  • Scalp electroencephalography (scEEG) suffers from noise and low resolution, limiting its clinical utility.
  • Intracranial electroencephalography (iEEG) provides high-resolution signals but is invasive.
  • Accurate detection of interictal epileptiform discharges (IEDs) is critical for epilepsy diagnosis and treatment.

Purpose of the Study:

  • To develop an EEG-to-EEG translation model to enhance scEEG signals by mapping them to iEEG signals.
  • To improve the resolution of scEEG data for better clinical analysis.
  • To facilitate more accurate detection of IEDs from enhanced scEEG data.

Main Methods:

  • A novel Variational Autoencoder combined with a Conditional Generative Adversarial Network (VAE-cGAN) was developed for EEG signal translation.
  • The VAE-cGAN model was trained to map low-resolution scEEG signals to high-resolution iEEG signals.
  • Interictal epileptiform discharges (IEDs) were detected from the translated iEEG signals.

Main Results:

  • The VAE-cGAN model successfully translated scEEG to iEEG, enhancing signal resolution.
  • IED detection from the translated iEEG signals achieved a classification accuracy of 76%.
  • This represents an improvement of 11%, 8%, and 3% over previous regression and autoencoder-based mapping models.

Conclusions:

  • The proposed VAE-cGAN model offers a promising non-invasive method for enhancing scEEG data quality.
  • Improved iEEG signal resolution facilitates more accurate detection of critical neurological events like IEDs.
  • This approach has the potential to advance epilepsy diagnosis and monitoring through improved EEG analysis.