Non-invasive Neural Decoding in Source Reconstructed Brain Space

Yonatan Gideoni, Ryan Charles Timms, Oiwi Parker Jones

Keywords: Neural decoding, MEG, brain decoding, structured learning, brain computer interface

TL;DR: Non-invasive neural decoding can use classical deep learning techniques using source space as a geometrically structured input

Abstract: 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.

Primary Area: applications to neuroscience & cognitive science

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Submission Number: 10475