Northeastern University, Boston, Massachusetts, United States of America.
PLoS One. 2013 Jul 3;8(7):e67692. doi: 10.1371/journal.pone.0067692. Print 2013.
We introduce the notion of Electric Field Encephalography (EFEG) based on measuring electric fields of the brain and demonstrate, using computer modeling, that given the appropriate electric field sensors this technique may have significant advantages over the current EEG technique. Unlike EEG, EFEG can be used to measure brain activity in a contactless and reference-free manner at significant distances from the head surface. Principal component analysis using simulated cortical sources demonstrated that electric field sensors positioned 3 cm away from the scalp and characterized by the same signal-to-noise ratio as EEG sensors provided the same number of uncorrelated signals as scalp EEG. When positioned on the scalp, EFEG sensors provided 2-3 times more uncorrelated signals. This significant increase in the number of uncorrelated signals can be used for more accurate assessment of brain states for non-invasive brain-computer interfaces and neurofeedback applications. It also may lead to major improvements in source localization precision. Source localization simulations for the spherical and Boundary Element Method (BEM) head models demonstrated that the localization errors are reduced two-fold when using electric fields instead of electric potentials. We have identified several techniques that could be adapted for the measurement of the electric field vector required for EFEG and anticipate that this study will stimulate new experimental approaches to utilize this new tool for functional brain research.
我们提出了基于测量大脑电场的电场脑电图(EFEG)的概念,并通过计算机建模证明,在使用适当的电场传感器的情况下,该技术可能比当前的脑电图技术具有显著优势。与 EEG 不同,EFEG 可以以非接触式和无参考的方式在距离头部表面有显著距离的地方测量大脑活动。使用模拟皮质源的主成分分析表明,位于头皮 3 厘米处且具有与 EEG 传感器相同信噪比的电场传感器提供了与头皮 EEG 相同数量的不相关信号。当置于头皮上时,EFEG 传感器提供了 2-3 倍的不相关信号。不相关信号数量的这种显著增加可用于更准确地评估非侵入性脑机接口和神经反馈应用的大脑状态。它还可能导致源定位精度的重大提高。对于球形和边界元法(BEM)头部模型的源定位模拟表明,使用电场而不是电势时,定位误差降低了两倍。我们已经确定了几种可用于测量 EFEG 所需的电场矢量的技术,并预计这项研究将激发新的实验方法,以利用这一新工具进行功能大脑研究。