论文标题
脑电图到fMRI合成受益于电极关系的注意图
EEG to fMRI Synthesis Benefits from Attentional Graphs of Electrode Relationships
论文作者
论文摘要
地形结构代表实体之间的连接，并提供了复杂系统的全面设计。目前，这些结构用于发现神经元和血液动力学活性的相关性。在这项工作中，我们将它们与神经加工技术结合在一起，以使用电生理活性来检索血液动力学。为此，我们使用傅立叶功能，注意机制，模式之间的共享空间以及在潜在表示中的样式融合。通过结合这些技术，我们提出了几种模型，这些模型在静止状态和基于任务的录音设置中显着优于此任务的最新最先进的模型。我们报告哪种EEG电极与回归任务最相关，哪些关系对其最大的关系影响最大。此外，我们观察到头皮的血液动力学活性与亚皮质区域相反，与学习的共享空间有关。总体而言，这些结果表明，EEG电极关系对于保留血液动力学检索所需的信息至关重要。
Topographical structures represent connections between entities and provide a comprehensive design of complex systems. Currently these structures are used to discover correlates of neuronal and haemodynamical activity. In this work, we incorporate them with neural processing techniques to perform regression, using electrophysiological activity to retrieve haemodynamics. To this end, we use Fourier features, attention mechanisms, shared space between modalities and incorporation of style in the latent representation. By combining these techniques, we propose several models that significantly outperform current state-of-the-art of this task in resting state and task-based recording settings. We report which EEG electrodes are the most relevant for the regression task and which relations impacted it the most. In addition, we observe that haemodynamic activity at the scalp, in contrast with sub-cortical regions, is relevant to the learned shared space. Overall, these results suggest that EEG electrode relationships are pivotal to retain information necessary for haemodynamical activity retrieval.
