Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Fort Detrick, MD, USA.
University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
J Sleep Res. 2018 Feb;27(1):98-102. doi: 10.1111/jsr.12576. Epub 2017 Jun 28.
Electroencephalography (EEG) recordings during sleep are often contaminated by muscle and ocular artefacts, which can affect the results of spectral power analyses significantly. However, the extent to which these artefacts affect EEG spectral power across different sleep states has not been quantified explicitly. Consequently, the effectiveness of automated artefact-rejection algorithms in minimizing these effects has not been characterized fully. To address these issues, we analysed standard 10-channel EEG recordings from 20 subjects during one night of sleep. We compared their spectral power when the recordings were contaminated by artefacts and after we removed them by visual inspection or by using automated artefact-rejection algorithms. During both rapid eye movement (REM) and non-REM (NREM) sleep, muscle artefacts contaminated no more than 5% of the EEG data across all channels. However, they corrupted delta, beta and gamma power levels substantially by up to 126, 171 and 938%, respectively, relative to the power level computed from artefact-free data. Although ocular artefacts were infrequent during NREM sleep, they affected up to 16% of the frontal and temporal EEG channels during REM sleep, primarily corrupting delta power by up to 33%. For both REM and NREM sleep, the automated artefact-rejection algorithms matched power levels to within ~10% of the artefact-free power level for each EEG channel and frequency band. In summary, although muscle and ocular artefacts affect only a small fraction of EEG data, they affect EEG spectral power significantly. This suggests the importance of using artefact-rejection algorithms before analysing EEG data.
睡眠期间的脑电图（EEG）记录常常受到肌肉和眼动伪迹的干扰，这会显著影响频谱功率分析的结果。然而，这些伪迹在不同睡眠状态下对 EEG 频谱功率的影响程度尚未明确量化。因此，自动伪迹剔除算法在最大限度减少这些影响方面的有效性尚未得到充分描述。为了解决这些问题，我们分析了 20 名受试者在一夜睡眠期间的标准 10 通道 EEG 记录。我们比较了记录受到伪迹污染和通过视觉检查或使用自动伪迹剔除算法去除伪迹后的频谱功率。在快速眼动（REM）和非快速眼动（NREM）睡眠期间，肌肉伪迹在所有通道上仅污染了不超过 5%的 EEG 数据。然而，与从无伪迹数据计算得出的功率水平相比，它们使 delta、beta 和 gamma 功率水平分别受到高达 126%、171%和 938%的实质性污染。尽管在 NREM 睡眠期间眼动伪迹很少见，但它们在 REM 睡眠期间影响了多达 16%的额部和颞部 EEG 通道，主要使 delta 功率受到高达 33%的影响。对于 REM 和 NREM 睡眠，自动伪迹剔除算法将每个 EEG 通道和频带的功率水平匹配到无伪迹功率水平的~10%以内。总之，尽管肌肉和眼动伪迹仅影响 EEG 数据的一小部分，但它们对 EEG 频谱功率有显著影响。这表明在分析 EEG 数据之前使用伪迹剔除算法的重要性。
