Klados Manousos A, Bamidis Panagiotis D
Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany; Lab of Medical Physics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Lab of Medical Physics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Data Brief. 2016 Jun 29;8:1004-6. doi: 10.1016/j.dib.2016.06.032. eCollection 2016 Sep.
Artifact rejection techniques are used to recover the brain signals underlying artifactual electroencephalographic (EEG) segments. Although over the last few years many different artifact rejection techniques have been proposed (http://dx.doi.org/10.1109/JSEN.2011.2115236[1], http://dx.doi.org/10.1016/j.clinph.2006.09.003[2], http://dx.doi.org/10.3390/e16126553[3]), none has been established as a gold standard so far, because assessing their performance is difficult and subjective (http://dx.doi.org/10.1109/ITAB.2009.5394295[4], http://dx.doi.org/10.1016/j.bspc.2011.02.001[5], http://dx.doi.org/10.1007/978-3-540-89208-3_300. [6]). This limitation is mainly based on the fact that the underlying artifact-free brain signal is unknown, so there is no objective way to measure how close the retrieved signal is to the real one. This article solves the aforementioned problem by presenting a semi-simulated EEG dataset, where artifact-free EEG signals are manually contaminated with ocular artifacts, using a realistic head model. The significant part of this dataset is that it contains the pre-contamination EEG signals, so the brain signals underlying the EOG artifacts are known and thus the performance of every artifact rejection technique can be objectively assessed.
伪迹去除技术用于恢复人工脑电图（EEG）片段背后的脑信号。尽管在过去几年中已经提出了许多不同的伪迹去除技术（http://dx.doi.org/10.1109/JSEN.2011.2115236[1]，http://dx.doi.org/10.1016/j.clinph.2006.09.003[2]，http://dx.doi.org/10.3390/e16126553[3]），但到目前为止还没有一种技术被确立为金标准，因为评估它们的性能既困难又主观（http://dx.doi.org/10.1109/ITAB.2009.5394295[4]，http://dx.doi.org/10.1016/j.bspc.2011.02.001[5]，http://dx.doi.org/10.1007/978-3-540-89208-3_300. [6]）。这种局限性主要基于这样一个事实，即潜在的无伪迹脑信号是未知的，因此没有客观的方法来衡量检索到的信号与真实信号的接近程度。本文通过提出一个半模拟的EEG数据集来解决上述问题，在该数据集中，使用逼真的头部模型将无伪迹的EEG信号手动添加眼动伪迹。该数据集的重要之处在于它包含污染前的EEG信号，因此眼电（EOG）伪迹背后的脑信号是已知的，从而可以客观地评估每种伪迹去除技术的性能。
