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

TY - JOUR

T1 - EEG-to-EEG

T2 - Scalp-to-Intracranial EEG Translation Using a Combination of Variational Autoencoder and Generative Adversarial Networks

AU - Abdi-Sargezeh, Bahman

AU - Shirani, Sepehr

AU - Valentin, Antonio

AU - Alarcon, Gonzalo

AU - Sanei, Saeid

N1 - Publisher Copyright: © 2025 by the authors.

PY - 2025/1/16

Y1 - 2025/1/16

N2 - A generative adversarial network (GAN) makes it possible to map a data sample from one domain to another one. It has extensively been employed in image-to-image and text-to image translation. We propose an EEG-to-EEG translation model to map the scalp-mounted EEG (scEEG) sensor signals to intracranial EEG (iEEG) sensor signals recorded by foramen ovale sensors inserted into the brain. The model is based on a GAN structure in which a conditional GAN (cGAN) is combined with a variational autoencoder (VAE), named as VAE-cGAN. scEEG sensors are plagued by noise and suffer from low resolution. On the other hand, iEEG sensor recordings enjoy high resolution. Here, we consider the task of mapping the scEEG sensor information to iEEG sensors to enhance the scEEG resolution. In this study, our EEG data contain epileptic interictal epileptiform discharges (IEDs). The identification of IEDs is crucial in clinical practice. Here, the proposed VAE-cGAN is firstly employed to map the scEEG to iEEG. Then, the IEDs are detected from the resulting iEEG. Our model achieves a classification accuracy of 76%, an increase of, respectively, 11%, 8%, and 3% over the previously proposed least-square regression, asymmetric autoencoder, and asymmetric-symmetric autoencoder mapping models.

AB - A generative adversarial network (GAN) makes it possible to map a data sample from one domain to another one. It has extensively been employed in image-to-image and text-to image translation. We propose an EEG-to-EEG translation model to map the scalp-mounted EEG (scEEG) sensor signals to intracranial EEG (iEEG) sensor signals recorded by foramen ovale sensors inserted into the brain. The model is based on a GAN structure in which a conditional GAN (cGAN) is combined with a variational autoencoder (VAE), named as VAE-cGAN. scEEG sensors are plagued by noise and suffer from low resolution. On the other hand, iEEG sensor recordings enjoy high resolution. Here, we consider the task of mapping the scEEG sensor information to iEEG sensors to enhance the scEEG resolution. In this study, our EEG data contain epileptic interictal epileptiform discharges (IEDs). The identification of IEDs is crucial in clinical practice. Here, the proposed VAE-cGAN is firstly employed to map the scEEG to iEEG. Then, the IEDs are detected from the resulting iEEG. Our model achieves a classification accuracy of 76%, an increase of, respectively, 11%, 8%, and 3% over the previously proposed least-square regression, asymmetric autoencoder, and asymmetric-symmetric autoencoder mapping models.

KW - Humans

KW - Electroencephalography/methods

KW - Scalp/physiology

KW - Neural Networks, Computer

KW - Signal Processing, Computer-Assisted

KW - Algorithms

KW - Brain/physiopathology

KW - Epilepsy/diagnosis

UR - http://www.scopus.com/inward/record.url?scp=85215811795&partnerID=8YFLogxK

U2 - 10.3390/s25020494

DO - 10.3390/s25020494

M3 - Article

C2 - 39860864

SN - 1424-8220

VL - 25

JO - Sensors (Basel, Switzerland)

JF - Sensors (Basel, Switzerland)

IS - 2

M1 - 494

ER -