Lyzhko E, Hamid L, Makhortykh S, Moliadze V, Siniatchkin M
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:1926-9. doi: 10.1109/EMBC.2015.7318760.
The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) is a powerful tool to investigate brain excitability and information processing in brain networks. However, EEG-TMS recordings are challenging because EEG is contaminated by powerful TMS-related artifacts. Because of these artifacts, different EEG-driven analyses (for instance, source analysis and analysis of information flow on the sensors and source level) reveal incorrect results. The aim of this study was to remove ocular artifacts from TMS-EEG recordings following stimulation of motor cortex using three independent component analysis (ICA) algorithms and to evaluate the effectiveness of these algorithms. We showed that the temporal ICA algorithm better separates those components that contain time-locked eye blink artifacts.
经颅磁刺激（TMS）与脑电图（EEG）相结合是研究大脑兴奋性和脑网络信息处理的有力工具。然而，脑电图-经颅磁刺激记录具有挑战性，因为脑电图会被与经颅磁刺激相关的强大伪迹所污染。由于这些伪迹，不同的脑电图驱动分析（例如，源分析以及传感器和源水平上的信息流分析）会得出错误结果。本研究的目的是使用三种独立成分分析（ICA）算法去除运动皮层刺激后经颅磁刺激-脑电图记录中的眼部伪迹，并评估这些算法的有效性。我们表明，时间独立成分分析算法能更好地分离那些包含与眨眼同步的伪迹的成分。
