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首页> 外文会议>Annual International Conference of the IEEE Engineering in Medicine and Biology Society >Removal of eye-blinking artifacts by ICA in cross-modal long-term EEG recording
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Removal of eye-blinking artifacts by ICA in cross-modal long-term EEG recording

机译：ICA在交叉模式长期脑电记录中消除眨眼的伪影

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摘要

Independent Component Analysis (ICA) has became the most popular method to remove eye-blinking artifacts from electroencephalogram (EEG) recording. For long term EEG recording, ICA was commonly considered to costing a lot of computation time. Furthermore, with no ground truth, the discussion about the quality of ICA decomposition in a nonstationary environment was specious. In this study, we investigated the "signal" (P300 waveform) and the "noise" (averaged eye-blinking artifacts) on a cross-modal long-term EEG recording to evaluate the efficiency and effectiveness of different methods on ICA eye-blinking artifacts removal. As a result, it was found that, firstly, down sampling is an effective way to reduce the computation time in ICA. Appropriate down sampling ratio could speed up ICA computation 200 times and keep the decomposition performance stable, in which the computation time of ICA decomposition on a 2800 s EEG recording was less than 5 s. Secondly, dimension reduction by PCA was also a way to improve the efficiency and effectiveness of ICA. Finally, the comparison by cropping the dataset indicated that performing ICA on each run of the experiment separately would achieve a better result for eye-blinking artifacts removal than using all the EEG data input for ICA.
机译：独立分量分析（ICA）已成为从脑电图（EEG）记录中去除眨眼伪像的最流行方法。对于长期的脑电图记录，ICA通常被认为要花费大量的计算时间。此外，由于没有事实依据，关于在非平稳环境中ICA分解的质量的讨论颇多。在这项研究中，我们研究了跨模式长期脑电图记录中的“信号”（P300波形）和“噪声”（平均眨眼伪像），以评估ICA眨眼不同方法的效率和有效性去除文物。结果，发现，首先，下采样是减少ICA中的计算时间的有效方法。适当降低采样率可加快ICA计算200倍，并保持分解性能稳定，其中在2800 s EEG记录上ICA分解的计算时间少于5 s。其次，通过PCA减少尺寸也是提高ICA效率和有效性的一种方法。最后，通过裁剪数据集进行的比较表明，与使用ICA的所有EEG数据输入相比，在实验的每次运行中分别执行ICA可获得更好的眨眼伪影去除效果。

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