An improved Generative Adversarial Network for Denoising EEG signals of brain-computer interface systems

Wang, Siyuan; Luo, You Xin; Shen, Hui

doi:10.1109/cac57257.2022.10055145

2022 China Automation Congress (CAC) 2022

DOI: 10.1109/cac57257.2022.10055145

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An improved Generative Adversarial Network for Denoising EEG signals of brain-computer interface systems

Siyuan Wang

You Xin Luo

Hui Shen

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2024

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Cited by 1 publication

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DL-based multi-artifact EEG denoising exploiting spectral information

Gabardi,

Saibene,

Gasparini

et al. 2024

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The artifacts affecting electroencephalographic (EEG) signals may undermine the correct interpretation of neural data that are used in a variety of applications spanning from diagnosis support systems to recreational brain-computer interfaces. Therefore, removing or - at least - reducing the noise content in respect to the actual brain activity data becomes of fundamental importance. However, manual removal of artifacts is not always applicable and appropriate, and sometimes the standard denoising techniques may encounter problems when dealing with noise frequency components overlapping with neural responses. In recent years, deep learning (DL) based denoising strategies have been developed to overcome these challenges and learn noise-related patterns to better discriminate actual EEG signals from artifact-related data. This study presents a novel DL-based EEG denoising model that leverages the prior knowledge on noise spectral features to adaptively compute optimal convolutional filters for multi-artifact noise removal. The proposed strategy is evaluated on a state-of-the-art benchmark dataset, namely EEGdenoiseNet, and achieves comparable to better performances in respect to other literature works considering both temporal and spectral metrics, providing a unique solution to remove muscle or ocular artifacts without needing a specific training on a particular artifact type.

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DL-based multi-artifact EEG denoising exploiting spectral information

Gabardi,

Saibene,

Gasparini

et al. 2024

View full text Add to dashboard Cite

show abstract

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An improved Generative Adversarial Network for Denoising EEG signals of brain-computer interface systems

Cited by 1 publication

References 17 publications

DL-based multi-artifact EEG denoising exploiting spectral information

DL-based multi-artifact EEG denoising exploiting spectral information

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