mlsanityData to 7 Oct 2026

EEG to fMRI Synthesis Benefits from Attentional Graphs of Electrode Relationships

arXiv 2203.03481 ↗First posted 7 Mar 2022q-bio.NC cs.LGcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 11 of its 53 references that are in the map. First posted on arXiv 7 Mar 2022.

Builds on

11 of 53 references.

References · 11

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  3. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,170
  4. 2018DARTS: Differentiable Architecture Searchcited by 1,782
  5. 2021StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesiscited by 477
  6. 2018Generative Adversarial Network in Medical Imaging: A Reviewcited by 257
  7. 2016Medical Image Synthesis with Context-Aware Generative Adversarial Networkscited by 91
  8. 2017Deep MR to CT Synthesis using Unpaired Datacited by 86
  9. 2018Dist-GAN: An Improved GAN using Distance Constraintscited by 26
  10. 2022Learning strides in convolutional neural networkscited by 25
  11. 2021Robust learning from corrupted EEG with dynamic spatial filteringcited by 14

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 2.

7 papers.