  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.
