Tozzi Arturo, Peters James F
Center for Nonlinear Science, University of North Texas, 1155 Union Circle, #311427, Denton, TX 76203-5017 USA.
Department of Electrical and Computer Engineering, University of Manitoba, 75A Chancellor's Circle, Winnipeg, MB R3T 5V6 Canada.
Cogn Neurodyn. 2024 Jun;18(3):1209-1214. doi: 10.1007/s11571-023-09978-4. Epub 2023 May 15.
The single macroscopic flow on the boundary of a closed curve equals the sum of the countless microscopic flows in the enclosed area. According to the dictates of the Green's theorem, the counterclockwise movements on the border of a two-dimensional shape must equal all the counterclockwise movements taking place inside the shape. This mathematical approach might be useful to analyse neuroscientific data sets for its potential capability to describe the whole cortical activity in terms of electric flows occurring in peripheral brain areas. Given a map of raw EEG data to coloured ovals in which different colours stand for different amplitudes, the theorem suggests that the sum of the electric amplitudes measured inside every oval equals the amplitudes measured just on the oval's edge. This means that the collection of the vector fields detected from the scalp can be described by a novel, single parameter summarizing the counterclockwise electric flow detected in the outer electrodes. To evaluate the predictive power of this parameter, in a pilot study we investigated EEG traces from ten young females performing Raven's intelligence tests of various complexity, from easy tasks (n = 5) to increasingly complex tasks (n = 5). Despite the seemingly unpredictable behavior of EEG electric amplitudes, the novel parameter proved to be a valuable tool to to discriminate between the two groups and detect hidden, statistically significant differences. We conclude that the application of this promising parameter could be expanded to assess also data sets extracted from neurotechniques other than EEG.
封闭曲线边界上的单一宏观流等于封闭区域内无数微观流的总和。根据格林定理,二维形状边界上的逆时针运动必须等于该形状内部发生的所有逆时针运动。这种数学方法可能有助于分析神经科学数据集,因为它有潜力根据外周脑区中发生的电流来描述整个皮层活动。给定一个将原始脑电图数据映射到彩色椭圆形的图,其中不同颜色代表不同幅度,该定理表明每个椭圆形内部测量的电幅度之和等于在椭圆形边缘测量的幅度。这意味着从头皮检测到的矢量场集合可以用一个新的单参数来描述,该参数总结了在外围电极中检测到的逆时针电流。为了评估这个参数的预测能力,在一项初步研究中,我们调查了10名年轻女性在进行各种复杂程度的瑞文智力测试时的脑电图轨迹,从简单任务(n = 5)到越来越复杂的任务(n = 5)。尽管脑电图电幅度的行为看似不可预测,但这个新参数被证明是区分两组并检测隐藏的、具有统计学显著差异的有价值工具。我们得出结论,这个有前景的参数的应用可以扩展到评估从脑电图以外的神经技术提取的数据集。