EEG-to-EEG: Scalp-to-Intracranial EEG Translation Using a Combination of Variational Autoencoder and Generative Adversarial Networks
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 intracrani...
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2025-01-01
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| author | Bahman Abdi-Sargezeh Sepehr Shirani Antonio Valentin Gonzalo Alarcon Saeid Sanei |
| author_facet | Bahman Abdi-Sargezeh Sepehr Shirani Antonio Valentin Gonzalo Alarcon Saeid Sanei |
| author_sort | Bahman Abdi-Sargezeh |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8be98c00ffa749d9960b274c7ad9bacc |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-8be98c00ffa749d9960b274c7ad9bacc2025-01-24T13:49:08ZengMDPI AGSensors1424-82202025-01-0125249410.3390/s25020494EEG-to-EEG: Scalp-to-Intracranial EEG Translation Using a Combination of Variational Autoencoder and Generative Adversarial NetworksBahman Abdi-Sargezeh0Sepehr Shirani1Antonio Valentin2Gonzalo Alarcon3Saeid Sanei4Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 2JD, UKDepartment of Clinical Neuroscience, King’s College London, London WC2R 2LS, UKDepartment of Clinical Neuroscience, King’s College London, London WC2R 2LS, UKSchool of Medical Sciences, University of Manchester, Manchester M13 9PL, UKDepartment of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UKA 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.https://www.mdpi.com/1424-8220/25/2/494generative adversarial networksIED detectioninterictal epileptiform dischargescalp-to-intracranial EEG translationvariational autoencoder |
| spellingShingle | Bahman Abdi-Sargezeh Sepehr Shirani Antonio Valentin Gonzalo Alarcon Saeid Sanei EEG-to-EEG: Scalp-to-Intracranial EEG Translation Using a Combination of Variational Autoencoder and Generative Adversarial Networks Sensors generative adversarial networks IED detection interictal epileptiform discharge scalp-to-intracranial EEG translation variational autoencoder |
| title | EEG-to-EEG: Scalp-to-Intracranial EEG Translation Using a Combination of Variational Autoencoder and Generative Adversarial Networks |
| title_full | EEG-to-EEG: Scalp-to-Intracranial EEG Translation Using a Combination of Variational Autoencoder and Generative Adversarial Networks |
| title_fullStr | EEG-to-EEG: Scalp-to-Intracranial EEG Translation Using a Combination of Variational Autoencoder and Generative Adversarial Networks |
| title_full_unstemmed | EEG-to-EEG: Scalp-to-Intracranial EEG Translation Using a Combination of Variational Autoencoder and Generative Adversarial Networks |
| title_short | EEG-to-EEG: Scalp-to-Intracranial EEG Translation Using a Combination of Variational Autoencoder and Generative Adversarial Networks |
| title_sort | eeg to eeg scalp to intracranial eeg translation using a combination of variational autoencoder and generative adversarial networks |
| topic | generative adversarial networks IED detection interictal epileptiform discharge scalp-to-intracranial EEG translation variational autoencoder |
| url | https://www.mdpi.com/1424-8220/25/2/494 |
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