Multivariate EEG analyses support high-resolution tracking of feature-based attentional selection.
Multivariate EEG analyses support high-resolution tracking of feature-based attentional selection.
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DOI：
10.1038/s41598-017-01911-0
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发表时间：
2017-05-15
影响因子：
4.6
通讯作者：
Eimer M
中科院分区：
文献类型：
--
作者：
Fahrenfort JJ;Grubert A;Olivers CNL;Eimer M
基于特征的选择的主要电生理标记是N2 pc，其是在180-200 ms左右出现的偏侧后负性。由于它依赖于半球的差异，它区分焦点注意力所在地的能力受到严重限制。在这里，我们证明了原始EEG数据的多变量分析提供了一个更细粒度的空间分布的基于特征的目标选择。当训练模式分类器以从EEG确定目标位置时，我们能够解码垂直中线上的目标位置，这是使用标准N2 pc方法无法实现的。接下来，我们使用前向编码模型来构建通道调谐函数，该函数描述了八位置显示中目标位置和多变量EEG之间的连续关系。该模型可以在空间上区分这些显示器中的各个目标位置，并且是完全可逆的，使我们能够为从未使用过的目标位置构建假设的地形激活图。当与从不同受试者组获得的真实的神经活动模式进行测试时，从正向模型构建的映射在统计上是不可区分的，从而为我们的模型提供了独立的验证。我们的研究结果表明，多变量EEG分析的权力，以跟踪基于特征的目标选择具有高的空间和时间精度。
The primary electrophysiological marker of feature-based selection is the N2pc, a lateralized posterior negativity emerging around 180–200 ms. As it relies on hemispheric differences, its ability to discriminate the locus of focal attention is severely limited. Here we demonstrate that multivariate analyses of raw EEG data provide a much more fine-grained spatial profile of feature-based target selection. When training a pattern classifier to determine target position from EEG, we were able to decode target positions on the vertical midline, which cannot be achieved using standard N2pc methodology. Next, we used a forward encoding model to construct a channel tuning function that describes the continuous relationship between target position and multivariate EEG in an eight-position display. This model can spatially discriminate individual target positions in these displays and is fully invertible, enabling us to construct hypothetical topographic activation maps for target positions that were never used. When tested against the real pattern of neural activity obtained from a different group of subjects, the constructed maps from the forward model turned out statistically indistinguishable, thus providing independent validation of our model. Our findings demonstrate the power of multivariate EEG analysis to track feature-based target selection with high spatial and temporal precision.