Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering.
Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering.
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DOI：
10.1049/htl2.12016
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发表时间：
2021-10
影响因子：
2.1
通讯作者：
Casson AJ
中科院分区：
文献类型：
--
作者：
Beach C;Li M;Balaban E;Casson AJ
本文提出了一种新的主动电极设计的脑电图（EEG）和心电图（ECG）传感器的基础上的惯性测量单元，以消除信号采集过程中的运动伪影。惯性测量单元不是从单个源测量整个记录单元的运动数据，而是连接到每个单独的EEG或ECG电极以收集局部运动数据。然后，该数据用于通过使用归一化最小均方自适应滤波来去除运动伪影。结果表明，所提出的有源电极设计可以减少来自EEG和ECG信号的运动污染，在胸部运动和头部摆动运动场景中。然而，发现性能变化，需要将算法与更复杂的信号处理配对，以识别在改善信号质量方面有益的场景。新的仪器硬件允许执行数据驱动的伪影去除，与广泛使用的盲源分离方法相比，提供了一种新的数据驱动方法，并有助于执行野生EEG记录。
This paper presents a new active electrode design for electroencephalogram (EEG) and electrocardiogram (ECG) sensors based on inertial measurement units to remove motion artefacts during signal acquisition. Rather than measuring motion data from a single source for the entire recording unit, inertial measurement units are attached to each individual EEG or ECG electrode to collect local movement data. This data is then used to remove the motion artefact by using normalised least mean square adaptive filtering. Results show that the proposed active electrode design can reduce motion contamination from EEG and ECG signals in chest movement and head swinging motion scenarios. However, it is found that the performance varies, necessitating the need for the algorithm to be paired with more sophisticated signal processing to identify scenarios where it is beneficial in terms of improving signal quality. The new instrumentation hardware allows data driven artefact removal to be performed, providing a new data driven approach compared to widely used blind‐source separation methods, and helps enable in the wild EEG recordings to be performed.
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影响因子：
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作者：
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通讯作者：
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