Crespo-Garcia Maite, Atienza Mercedes, Cantero Jose L
Laboratory of Functional Neuroscience, University Pablo de Olavide, Ctra. de Utrera, Km. 1, 41013, Seville, Spain.
Ann Biomed Eng. 2008 Mar;36(3):467-75. doi: 10.1007/s10439-008-9442-y. Epub 2008 Jan 29.
Muscle artifacts are typically associated with sleep arousals and awakenings in normal and pathological sleep, contaminating EEG recordings and distorting quantitative EEG results. Most EEG correction techniques focus on ocular artifacts but little research has been done on removing muscle activity from sleep EEG recordings. The present study was aimed at assessing the performance of four independent component analysis (ICA) algorithms (AMUSE, SOBI, Infomax, and JADE) to separate myogenic activity from EEG during sleep, in order to determine the optimal method. AMUSE, Infomax, and SOBI performed significantly better than JADE at eliminating muscle artifacts over temporal regions, but AMUSE was independent of the signal-to-noise ratio over non-temporal regions and markedly faster than the remaining algorithms. AMUSE was further successful at separating muscle artifacts from spontaneous EEG arousals when applied on a real case during different sleep stages. The low computational cost of AMUSE, and its excellent performance with EEG arousals from different sleep stages supports this ICA algorithm as a valid choice to minimize the influence of muscle artifacts on human sleep EEG recordings.
肌肉伪迹通常与正常睡眠和病理睡眠中的睡眠觉醒及醒来相关，会污染脑电图（EEG）记录并扭曲定量脑电图结果。大多数脑电图校正技术专注于眼动伪迹，但在从睡眠脑电图记录中去除肌肉活动方面的研究很少。本研究旨在评估四种独立成分分析（ICA）算法（AMUSE、SOBI、Infomax和JADE）在睡眠期间从脑电图中分离肌源性活动的性能，以确定最佳方法。在消除颞区的肌肉伪迹方面，AMUSE、Infomax和SOBI的表现明显优于JADE，但在非颞区，AMUSE与信噪比无关，且比其余算法明显更快。当应用于不同睡眠阶段的真实案例时，AMUSE在从自发脑电图觉醒中分离肌肉伪迹方面也取得了成功。AMUSE的低计算成本及其在不同睡眠阶段的脑电图觉醒中的出色表现，支持将这种ICA算法作为最小化肌肉伪迹对人类睡眠脑电图记录影响的有效选择。
