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Auto-Denoising for EEG Signals Using Generative Adversarial Network

The brain–computer interface (BCI) has many applications in various fields. In EEG-based research, an essential step is signal denoising. In this paper, a generative adversarial network (GAN)-based denoising method is proposed to denoise the multichannel EEG signal automatically. A new loss function...

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Detalles Bibliográficos
Autores principales: An, Yang, Lam, Hak Keung, Ling, Sai Ho
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8914841/
https://www.ncbi.nlm.nih.gov/pubmed/35270895
http://dx.doi.org/10.3390/s22051750
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author An, Yang
Lam, Hak Keung
Ling, Sai Ho
author_facet An, Yang
Lam, Hak Keung
Ling, Sai Ho
author_sort An, Yang
collection PubMed
description The brain–computer interface (BCI) has many applications in various fields. In EEG-based research, an essential step is signal denoising. In this paper, a generative adversarial network (GAN)-based denoising method is proposed to denoise the multichannel EEG signal automatically. A new loss function is defined to ensure that the filtered signal can retain as much effective original information and energy as possible. This model can imitate and integrate artificial denoising methods, which reduces processing time; hence it can be used for a large amount of data processing. Compared to other neural network denoising models, the proposed model has one more discriminator, which always judges whether the noise is filtered out. The generator is constantly changing the denoising way. To ensure the GAN model generates EEG signals stably, a new normalization method called sample entropy threshold and energy threshold-based (SETET) normalization is proposed to check the abnormal signals and limit the range of EEG signals. After the denoising system is established, although the denoising model uses the different subjects’ data for training, it can still apply to the new subjects’ data denoising. The experiments discussed in this paper employ the HaLT public dataset. Correlation and root mean square error (RMSE) are used as evaluation criteria. Results reveal that the proposed automatic GAN denoising network achieves the same performance as the manual hybrid artificial denoising method. Moreover, the GAN network makes the denoising process automatic, representing a significant reduction in time.
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spelling pubmed-89148412022-03-12 Auto-Denoising for EEG Signals Using Generative Adversarial Network An, Yang Lam, Hak Keung Ling, Sai Ho Sensors (Basel) Article The brain–computer interface (BCI) has many applications in various fields. In EEG-based research, an essential step is signal denoising. In this paper, a generative adversarial network (GAN)-based denoising method is proposed to denoise the multichannel EEG signal automatically. A new loss function is defined to ensure that the filtered signal can retain as much effective original information and energy as possible. This model can imitate and integrate artificial denoising methods, which reduces processing time; hence it can be used for a large amount of data processing. Compared to other neural network denoising models, the proposed model has one more discriminator, which always judges whether the noise is filtered out. The generator is constantly changing the denoising way. To ensure the GAN model generates EEG signals stably, a new normalization method called sample entropy threshold and energy threshold-based (SETET) normalization is proposed to check the abnormal signals and limit the range of EEG signals. After the denoising system is established, although the denoising model uses the different subjects’ data for training, it can still apply to the new subjects’ data denoising. The experiments discussed in this paper employ the HaLT public dataset. Correlation and root mean square error (RMSE) are used as evaluation criteria. Results reveal that the proposed automatic GAN denoising network achieves the same performance as the manual hybrid artificial denoising method. Moreover, the GAN network makes the denoising process automatic, representing a significant reduction in time. MDPI 2022-02-23 /pmc/articles/PMC8914841/ /pubmed/35270895 http://dx.doi.org/10.3390/s22051750 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
An, Yang
Lam, Hak Keung
Ling, Sai Ho
Auto-Denoising for EEG Signals Using Generative Adversarial Network
title Auto-Denoising for EEG Signals Using Generative Adversarial Network
title_full Auto-Denoising for EEG Signals Using Generative Adversarial Network
title_fullStr Auto-Denoising for EEG Signals Using Generative Adversarial Network
title_full_unstemmed Auto-Denoising for EEG Signals Using Generative Adversarial Network
title_short Auto-Denoising for EEG Signals Using Generative Adversarial Network
title_sort auto-denoising for eeg signals using generative adversarial network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8914841/
https://www.ncbi.nlm.nih.gov/pubmed/35270895
http://dx.doi.org/10.3390/s22051750
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