mlsanityData to 14 Oct 2026

Non-invasive Neural Decoding in Source Reconstructed Brain Space

arXiv 2410.19838 ↗First posted 20 Oct 2024eess.SP cs.LGcited by 2

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Abstract (arXiv)

Non-invasive brainwave decoding is usually done using Magneto/Electroencephalography (MEG/EEG) sensor measurements as inputs. This makes combining datasets and building models with inductive biases difficult as most datasets use different scanners and the sensor arrays have a nonintuitive spatial structure. In contrast, fMRI scans are acquired directly in brain space, a voxel grid with a typical structured input representation. By using established techniques to reconstruct the sensors' sources' neural activity it is possible to decode from voxels for MEG data as well. We show that this enables spatial inductive biases, spatial data augmentations, better interpretability, zero-shot generalisation between datasets, and data harmonisation.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 26 of its 92 references that are in the map. First posted on arXiv 20 Oct 2024.

Builds on

26 of 92 references.

References · 26

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14 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Unlocking Non-Invasive Brain-to-Textcited by 15
  2. 2026CANDLE: Cortical Null-Space Decomposition for Noninvasive Brain Source Imagingcited by 0