Artifact removal in electroencephalography (EEG) is a longstanding challenge that significantly impacts neuroscientific analysis and brain-computer interface (BCI) performance. Tackling this problem demands advanced algorithms, extensive noisy-clean training data, and thorough evaluation strategies. This study presents the Artifact Removal Transformer (ART), an innovative EEG denoising model employing transformer architecture to adeptly capture the transient millisecond-scale dynamics characteristic of EEG signals. Our approach offers a holistic, end-to-end denoising solution for diverse artifact types in multichannel EEG data. We enhanced the generation of noisy-clean EEG data pairs using an independent component analysis, thus fortifying the training scenarios critical for effective supervised learning. We performed comprehensive validations using a wide range of open datasets from various BCI applications, employing metrics like mean squared error and signal-to-noise ratio, as well as sophisticated techniques such as source localization and EEG component classification. Our evaluations confirm that ART surpasses other deep-learning-based artifact removal methods, setting a new benchmark in EEG signal processing. This advancement not only boosts the accuracy and reliability of artifact removal but also promises to catalyze further innovations in the field, facilitating the study of brain dynamics in naturalistic environments.

翻译：脑电图（EEG）中的伪影去除是一个长期存在的挑战，显著影响神经科学分析和脑机接口（BCI）性能。解决这一问题需要先进的算法、大量带噪声-干净训练数据以及全面的评估策略。本研究提出伪影去除Transformer（ART），这是一种创新的EEG去噪模型，采用Transformer架构以有效捕捉EEG信号特有的毫秒级瞬态动态特性。我们的方法为多通道EEG数据中多种伪影类型提供了一个整体的端到端去噪解决方案。我们通过独立成分分析改进了带噪声-干净EEG数据对的生成，从而强化了对有效监督学习至关重要的训练场景。我们使用来自不同BCI应用的大量开放数据集进行了全面验证，采用了均方误差和信噪比等指标，以及源定位和EEG成分分类等先进技术。我们的评估证实，ART超越了其他基于深度学习的伪影去除方法，为EEG信号处理设立了新的基准。这一进展不仅提高了伪影去除的准确性和可靠性，还有望推动该领域的进一步创新，促进自然环境下脑动态的研究。