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Real-time ocular artifacts removal of EEG data using a hybrid ICA-ANC approach

机译：使用ICA-ANC混合方法实时去除EEG数据

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Removal of ocular artifacts (OA) in real-time is an essential component in electroencephalography (EEG) based brain computer interface (BCI) applications. However, many proposed artifact removal methods are not applicable in real-time applications due to their time-consuming process. In this paper we propose a hybrid approach based on a new combination of independent component analysis (ICA) and adaptive noise cancellation (ANC). A particularly new feature of the proposed approach is the utilization of the ICA decomposition to extract the artifact source signal to be used in ANC based on neural networks. The method performs using a few EEG signals without requiring any additional electrodes (e.g. electrooculography). We show that the proposed approach is capable of effectively reducing the ocular artifacts in a negligible time delay well applicable in real-time BCI. In order to achieve reliable results, the proposed approach is evaluated using data recorded during cue-based BCI. The efficacy of the proposed approach in both offline and online performances is compared to several state of the art methods. The results demonstrate that the proposed approach outperforms the compared methods in terms of removal of OA and recovery of the underlying EEG. (C) 2016 Elsevier Ltd. All rights reserved.
机译：在基于脑电图（EEG）的脑计算机接口（BCI）应用中，实时去除眼部伪影（OA）是必不可少的组件。然而，由于其耗时的过程，许多提出的伪像去除方法不适用于实时应用。在本文中，我们提出了一种基于独立成分分析（ICA）和自适应噪声消除（ANC）的新组合的混合方法。所提出的方法的一个特别新的特征是利用ICA分解来提取要在基于神经网络的ANC中使用的伪像源信号。该方法使用一些EEG信号来执行，而不需要任何额外的电极（例如眼电图术）。我们表明，提出的方法能够在可忽略的时间延迟中有效地减少眼部伪影，并很好地适用于实时BCI。为了获得可靠的结果，使用基于提示的BCI期间记录的数据对提出的方法进行了评估。将所提出的方法在离线和在线性能中的功效与几种现有方法进行了比较。结果表明，所提出的方法在去除OA和恢复潜在的脑电图方面优于已比较的方法。 （C）2016 Elsevier Ltd.保留所有权利。

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