  In this work, we have adopted the diffusion-based image reconstruction
pipeline previously used for fMRI image reconstruction and applied it to
Electroencephalography (EEG). The EEG encoding method is very simple, and forms
a baseline from which more sophisticated EEG encoding methods can be compared.
We have also evaluated the fidelity of the generated image using the same
metrics used in the previous functional magnetic resonance imaging (fMRI) and
magnetoencephalography (MEG) works. Our results show that while the
reconstruction from EEG recorded to rapidly presented images is not as good as
reconstructions from fMRI to slower presented images, it holds a surprising
amount of information that could be applied in specific use cases. Also,
EEG-based image reconstruction works better in some categories-such as land
animals and food-than others, shedding new light on previous findings of EEG's
sensitivity to those categories and revealing potential for these methods to
further understand EEG responses to human visual coding. More investigation
should use longer-duration image stimulations to elucidate the later components
that might be salient to the different image categories.
