Hu Shiang, Lai Yongxiu, Valdes-Sosa Pedro A, Bringas-Vega Maria L, Yao Dezhong
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
J Neural Eng. 2018 Jan 25;15(2):026013. doi: 10.1088/1741-2552/aaa13f.
Human scalp electroencephalogram (EEG) is widely applied in cognitive neuroscience and clinical studies due to its non-invasiveness and ultra-high time resolution. However, the representativeness of the measured EEG potentials for the underneath neural activities is still a problem under debate. This study aims to investigate systematically how both reference montage and electrodes setup affect the accuracy of EEG potentials.
First, the standard EEG potentials are generated by the forward calculation with a single dipole in the neural source space, for eleven channel numbers (10, 16, 21, 32, 64, 85, 96, 128, 129, 257, 335). Here, the reference is the ideal infinity implicitly determined by forward theory. Then, the standard EEG potentials are transformed to recordings with different references including five mono-polar references (Left earlobe, Fz, Pz, Oz, Cz), and three re-references (linked mastoids (LM), average reference (AR) and reference electrode standardization technique (REST)). Finally, the relative errors between the standard EEG potentials and the transformed ones are evaluated in terms of channel number, scalp regions, electrodes layout, dipole source position and orientation, as well as sensor noise and head model.
Mono-polar reference recordings are usually of large distortions; thus, a re-reference after online mono-polar recording should be adopted in general to mitigate this effect. Among the three re-references, REST is generally superior to AR for all factors compared, and LM performs worst. REST is insensitive to head model perturbation. AR is subject to electrodes coverage and dipole orientation but no close relation with channel number.
These results indicate that REST would be the first choice of re-reference and AR may be an alternative option for high level sensor noise case. Our findings may provide the helpful suggestions on how to obtain the EEG potentials as accurately as possible for cognitive neuroscientists and clinicians.
人类头皮脑电图（EEG）因其非侵入性和超高时间分辨率，在认知神经科学和临床研究中得到广泛应用。然而，所测量的EEG电位对于其下神经活动的代表性仍是一个存在争议的问题。本研究旨在系统地探究参考导联和电极设置如何影响EEG电位的准确性。
首先，通过在神经源空间中使用单个偶极子进行正向计算，生成11种通道数（10、16、21、32、64、85、96、128、129、257、335）的标准EEG电位。这里，参考是由正向理论隐含确定的理想无穷远。然后，将标准EEG电位转换为具有不同参考的记录，包括五种单极参考（左耳耳垂、Fz、Pz、Oz、Cz）和三种重新参考（连锁乳突（LM）、平均参考（AR）和参考电极标准化技术（REST））。最后，根据通道数、头皮区域、电极布局、偶极子源位置和方向，以及传感器噪声和头部模型，评估标准EEG电位与转换后的电位之间的相对误差。
单极参考记录通常存在较大失真；因此，一般应在在线单极记录后采用重新参考来减轻这种影响。在三种重新参考中，对于所有比较因素，REST通常优于AR，而LM表现最差。REST对头部模型扰动不敏感。AR受电极覆盖范围和偶极子方向影响，但与通道数无密切关系。
这些结果表明，REST将是重新参考的首选，而AR可能是高电平传感器噪声情况下的替代选择。我们的发现可能为认知神经科学家和临床医生提供关于如何尽可能准确地获取EEG电位的有益建议。
