DL-based multi-artifact EEG denoising exploiting spectral information

Matteo Gabardi, Aurora Saibene, Francesca Gasparini, Daniele Rizzo, F. Stella
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Abstract

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.
利用频谱信息进行基于 DL 的多特征脑电图去噪
影响脑电图(EEG)信号的假象可能会影响对神经数据的正确解读,而这些数据被广泛应用于从诊断支持系统到娱乐性脑机接口等多个领域。因此,去除或至少减少与实际脑活动数据相关的噪声内容变得至关重要。然而,人工去除伪影并不总是适用和适当的,有时标准去噪技术在处理与神经响应重叠的噪声频率成分时可能会遇到问题。近年来,人们开发了基于深度学习(DL)的去噪策略,以克服这些挑战,并学习与噪声相关的模式,从而更好地将实际脑电信号与伪影相关数据区分开来。本研究提出了一种新颖的基于深度学习的脑电图去噪模型,该模型利用噪声频谱特征的先验知识,自适应地计算最佳卷积滤波器,以去除多伪迹噪声。所提出的策略在最先进的基准数据集(即 EEGdenoiseNet)上进行了评估,在考虑时间和频谱指标的情况下,取得了与其他文献作品相当甚至更好的性能,为去除肌肉或眼部伪影提供了独特的解决方案,而无需针对特定伪影类型进行专门训练。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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