Objective. When developing approaches for automatic preprocessing of electroencephalogram (EEG) signals in non-isolated demanding environment such as intensive care unit (ICU) or even outdoor environment, one of the major concerns is varying nature of characteristics of different artifacts in time, frequency and spatial domains, which in turn causes a simple approach to be not enough for reliable artifact removal. Considering this, current study aims to use correlation-driven mapping to improve artifact detection performance. Approach. A framework is proposed here for mapping signals from multichannel space (regardless of the number of EEG channels) into two-dimensional RGB space, in which the correlation of all EEG channels is simultaneously taken into account, and a deep convolutional neural network (CNN) model can then learn specific patterns in generated 2D representation related to specific artifact. Main results. The method with a classification accur...

中文翻译：

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用于检测脑电图内伪影的深度学习应用的相关驱动映射

客观的。在开发用于在诸如重症监护室 (ICU) 甚至室外环境等非隔离苛刻环境中自动预处理脑电图 (EEG) 信号的方法时，主要关注的问题之一是不同伪影在时间、频率和空间上的特性的不同性质域，这反过来又导致简单的方法不足以可靠地去除伪影。考虑到这一点，目前的研究旨在使用相关驱动映射来提高伪影检测性能。方法。这里提出了一个框架，用于将信号从多通道空间（不管 EEG 通道的数量）映射到二维 RGB 空间，其中同时考虑所有 EEG 通道的相关性，然后，深度卷积神经网络 (CNN) 模型可以在生成的与特定工件相关的 2D 表示中学习特定模式。主要结果。该方法具有分类准确...