视觉和听觉任务中脑电图记录线性建模的不同回归量

Carlos A. Mugruza-Vassallo
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引用次数: 5

摘要

在过去的5年里,使用层次线性模型来分析脑电图数据越来越多。到目前为止,还没有对不同模态下的线性建模进行明确的比较。因此,在视觉和听觉范式中观察到的具体差异是用线性建模计算的。通过线性模型中解释方差(R2)的决定系数在视觉和听觉模式中寻求。同时,绘制了视觉任务100 ~ 300 ms和听觉任务150 ~ 400 ms的ERP头皮序列。虽然这些范式使用不同的回归量,但两种范式都显示出可靠的R2特征和可靠的ERP头皮图。结果表明,视觉模态的R2值较大。与其他受试者的R2研究相比,听觉R2结果具有可靠的线性模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Different regressors for linear modelling of ElectroEncephaloGraphic recordings in visual and auditory tasks
The use of hierarchical linear modelling has been increasing in the last 5 years to analyze EEG data. Until now, no clear comparison on linear modelling in different modalities has been done. Therefore, specific differences observed in both visual and auditory paradigms were computed with linear modelling. The Coefficient of Determination through the explained variance (R2) in Linear Modelling was sought in visual and auditory modalities. ERP scalp series of time from 100 to 300 ms for the visual task and around 150 ms to 400 for the auditory task were also plotted. Although these paradigms use different regressors, both paradigms showed reliable R2 signatures across the participants and reliable ERP scalp maps. Results accounted for different magnitudes in greater R2 values for visual modality. Auditory R2 results appeared with a reliable linear modelling when compared with R2 studies in other subjects.
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