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.
@article{arxiv.2410.19838,
title = {Non-invasive Neural Decoding in Source Reconstructed Brain Space},
author = {Yonatan Gideoni and Ryan Charles Timms and Oiwi Parker Jones},
journal= {arXiv preprint arXiv:2410.19838},
year = {2024}
}