ART: 用于重构无噪声多通道脑电信号的去伪变压器

Chun-Hsiang Chuang, Kong-Yi Chang, Chih-Sheng Huang, Anne-Mei Bessas
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引用次数: 0

摘要

消除脑电图(EEG)中的伪影是一项长期存在的挑战,对神经科学分析和脑机接口(BCI)性能有重大影响。要解决这一问题,需要先进的算法、广泛的噪声清洁训练数据和全面的评估策略。本研究提出了去伪变压器(Artifact Removal Transformer,ART),这是一种创新的脑电图去噪模型,它采用变压器架构,能有效捕捉脑电图信号的毫秒级瞬态特征。我们的方法为多通道脑电图数据中的各种伪影类型提供了整体的端到端去噪解决方案。我们利用独立成分分析增强了噪声-清洁脑电图数据对的生成,从而强化了对有效监督学习至关重要的训练场景。我们使用来自各种 BCI 应用的广泛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,以及源定位和脑电图成分分类等复杂技术。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,为脑电图信号处理树立了新的标杆。这一进步不仅提高了去除伪影的准确性和可靠性,而且有望推动该领域的进一步创新,促进自然环境下的脑动力学研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ART: Artifact Removal Transformer for Reconstructing Noise-Free Multichannel Electroencephalographic Signals
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.
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