Department of Psychology, Carnegie Mellon University, Pittsburgh, USA.
Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, USA.
Sci Rep. 2017 Nov 24;7(1):16248. doi: 10.1038/s41598-017-16377-3.
Standard human EEG systems based on spatial Nyquist estimates suggest that 20-30 mm electrode spacing suffices to capture neural signals on the scalp, but recent studies posit that increasing sensor density can provide higher resolution neural information. Here, we compared "super-Nyquist" density EEG ("SND") with Nyquist density ("ND") arrays for assessing the spatiotemporal aspects of early visual processing. EEG was measured from 128 electrodes arranged over occipitotemporal brain regions (14 mm spacing) while participants viewed flickering checkerboard stimuli. Analyses compared SND with ND-equivalent subsets of the same electrodes. Frequency-tagged stimuli were classified more accurately with SND than ND arrays in both the time and the frequency domains. Representational similarity analysis revealed that a computational model of V1 correlated more highly with the SND than the ND array. Overall, SND EEG captured more neural information from visual cortex, arguing for increased development of this approach in basic and translational neuroscience.
基于空间奈奎斯特估计的标准人类脑电图系统表明,20-30mm 的电极间距足以在头皮上捕获神经信号,但最近的研究假设增加传感器密度可以提供更高分辨率的神经信息。在这里,我们比较了“超奈奎斯特密度脑电图”(SND)和奈奎斯特密度(ND)阵列,以评估早期视觉处理的时空方面。当参与者观看闪烁的棋盘刺激时,从排列在枕颞脑区的 128 个电极(14mm 间距)测量 EEG。分析比较了 SND 与相同电极的 ND 等效子集。在时间和频率域中,SND 比 ND 阵列更准确地对标记频率的刺激进行分类。表示相似性分析表明,V1 的计算模型与 SND 的相关性高于 ND 阵列。总的来说,SND EEG 从视觉皮层中捕获了更多的神经信息,这表明在基础和转化神经科学中应更多地开发这种方法。