Balancing Spectral, Temporal and Spatial Information for EEG-based
  Alzheimer's Disease Classification
Stephan Goerttler Fei He Min Wu
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 systematically 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 test various
dimension resolution configurations on two routine EEG datasets. We find that
spatial information is consistently more relevant than temporal information and
equally relevant as spectral information. These results emphasise the necessity
to consider spatial information for EEG-based AD classification. On our second
dataset, we further find that well-balanced feature resolutions boost
classification accuracy by up to 1.6%. Our resolution-based feature extraction
has the potential to improve AD classification specifically, and multivariate
signal classification generally.