Lepage Kyle Q, Kramer Mark A, Chu Catherine J
Boston University, Department of Mathematics and Statistics, Boston, MA, USA.
Massachusetts General Hospital, Boston, MA, USA.
J Neurosci Methods. 2014 Sep 30;235:101-16. doi: 10.1016/j.jneumeth.2014.05.008. Epub 2014 Jun 27.
The electroencephalogram (EEG) remains the primary tool for diagnosis of abnormal brain activity in clinical neurology and for in vivo recordings of human neurophysiology in neuroscience research. In EEG data acquisition, voltage is measured at positions on the scalp with respect to a reference electrode. When this reference electrode responds to electrical activity or artifact all electrodes are affected. Successful analysis of EEG data often involves re-referencing procedures that modify the recorded traces and seek to minimize the impact of reference electrode activity upon functions of the original EEG recordings.
We provide a novel, statistically robust procedure that adapts a robust maximum-likelihood type estimator to the problem of reference estimation, reduces the influence of neural activity from the re-referencing operation, and maintains good performance in a wide variety of empirical scenarios.
The performance of the proposed and existing re-referencing procedures are validated in simulation and with examples of EEG recordings. To facilitate this comparison, channel-to-channel correlations are investigated theoretically and in simulation.
The proposed procedure avoids using data contaminated by neural signal and remains unbiased in recording scenarios where physical references, the common average reference (CAR) and the reference estimation standardization technique (REST) are not optimal.
The proposed procedure is simple, fast, and avoids the potential for substantial bias when analyzing low-density EEG data.
脑电图（EEG）仍然是临床神经病学中诊断异常脑活动以及神经科学研究中人体神经生理学活体记录的主要工具。在EEG数据采集中，相对于参考电极在头皮上的位置测量电压。当该参考电极对电活动或伪迹做出反应时，所有电极都会受到影响。EEG数据的成功分析通常涉及重新参考程序，该程序会修改记录的迹线，并试图最小化参考电极活动对原始EEG记录功能的影响。
我们提供了一种新颖的、具有统计稳健性的程序，该程序将稳健的最大似然类型估计器应用于参考估计问题，减少重新参考操作中神经活动的影响，并在各种实际场景中保持良好性能。
在模拟和EEG记录示例中验证了所提出的和现有的重新参考程序的性能。为便于比较，从理论和模拟两方面研究了通道间相关性。
所提出的程序避免使用受神经信号污染的数据，并且在物理参考、公共平均参考（CAR）和参考估计标准化技术（REST）并非最佳的记录场景中保持无偏性。
所提出的程序简单、快速，并且在分析低密度EEG数据时避免了出现实质性偏差的可能性。
