  In modern neuroscience, functional magnetic resonance imaging (fMRI) has been
a crucial and irreplaceable tool that provides a non-invasive window into the
dynamics of whole-brain activity. Nevertheless, fMRI is limited by hemodynamic
blurring as well as high cost, immobility, and incompatibility with metal
implants. Electroencephalography (EEG) is complementary to fMRI and can
directly record the cortical electrical activity at high temporal resolution,
but has more limited spatial resolution and is unable to recover information
about deep subcortical brain structures. The ability to obtain fMRI information
from EEG would enable cost-effective, imaging across a wider set of brain
regions. Further, beyond augmenting the capabilities of EEG, cross-modality
models would facilitate the interpretation of fMRI signals. However, as both
EEG and fMRI are high-dimensional and prone to artifacts, it is currently
challenging to model fMRI from EEG. To address this challenge, we propose a
novel architecture that can predict fMRI signals directly from multi-channel
EEG without explicit feature engineering. Our model achieves this by
implementing a Sinusoidal Representation Network (SIREN) to learn frequency
information in brain dynamics from EEG, which serves as the input to a
subsequent encoder-decoder to effectively reconstruct the fMRI signal from a
specific brain region. We evaluate our model using a simultaneous EEG-fMRI
dataset with 8 subjects and investigate its potential for predicting
subcortical fMRI signals. The present results reveal that our model outperforms
a recent state-of-the-art model, and indicates the potential of leveraging
periodic activation functions in deep neural networks to model functional
neuroimaging data.
