  The prospect of future treatment warrants the development of cost-effective
screening for Alzheimer's disease (AD). A promising candidate in this regard is
electroencephalography (EEG), as it is one of the most economic imaging
modalities. Recent efforts in EEG analysis have shifted towards leveraging
spatial information, employing novel frameworks such as graph signal processing
or graph neural networks. Here, we investigate the importance of spatial
information relative to spectral or temporal information by varying the
proportion of each dimension for AD classification. To do so, we systematically
test various dimension resolution configurations on two routine EEG datasets.
Our findings show that spatial information is more important than temporal
information and equally valuable as spectral information. On the larger second
dataset, substituting spectral with spatial information even led to an increase
of 1.1% in accuracy, which emphasises the importance of spatial information for
EEG-based AD classification. We argue that our resolution-based feature
extraction has the potential to improve AD classification specifically, and
multivariate signal classification generally.
