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Decoding and Reconstructing the Focus of Spatial Attention from the Topography of Alpha-band Oscillations

机译:从Alpha波段振荡的地形解码和重构空间注意力的焦点

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摘要

Many aspects of perception and cognition are supported by activity in neural populations that are tuned to different stimulus features (e.g., orientation, spatial location, color). Goal-directed behavior, such as sustained attention, requires a mechanism for the selective prioritization of contextually appropriate representations. A candidate mechanism of sustained spatial attention is neural activity in the alpha band (8–13 Hz), whose power in the human EEG covaries with the focus of covert attention. Here, we applied an inverted encoding model to assess whether spatially selective neural responses could be recovered from the topography of alpha-band oscillations during spatial attention. Participants were cued to covertly attend to one of six spatial locations arranged concentrically around fixation while EEG was recorded. A linear classifier applied to EEG data during sustained attention demonstrated successful classification of the attended location from the topography of alpha power, although not from other frequency bands. We next sought to reconstruct the focus of spatial attention over time by applying inverted encoding models to the topography of alpha power and phase. Alpha power, but not phase, allowed for robust reconstructions of the specific attended location beginning around 450 msec postcue, an onset earlier than previous reports. These results demonstrate that posterior alpha-band oscillations can be used to track activity in feature-selective neural populations with high temporal precision during the deployment of covert spatial attention.
机译:感知和认知的许多方面都受到神经群体活动的支持,这些活动已针对不同的刺激特征(例如,方向,空间位置,颜色)进行了调整。以目标为导向的行为,例如持续的注意力,需要一种机制来选择性地确定上下文相关表示的优先级。持续的空间注意力的候选机制是在α波段(8–13 Hz)中的神经活动,其在人类脑电图中的力量会随着隐秘注意力的变化而变化。在这里,我们应用了反向编码模型来评估在空间关注过程中是否可以从α波段振荡的地形中恢复出空间选择性神经反应。要求参与者在记录脑电图的过程中秘密参与同心固定周围六个空间位置之一。在持续关注期间将线性分类器应用于EEG数据表明,尽管不是从其他频段,但根据α功率的拓扑结构成功地对了参与位置进行了分类。接下来,我们试图通过将反向编码模型应用于Alpha功率和相位的拓扑来重建空间关注的焦点。 Alpha功率(但不是相位)允许对特定的有人看守位置进行强大的重建,该位置大约在事后450毫秒开始,比以前的报告早了。这些结果表明,在隐秘空间注意力的部署过程中,后方的α波段振荡可用于以高时间精度跟踪特征选择神经种群中的活动。

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