  {Introduction: } Dementia is a neurological disorder associated with aging
that can cause a loss of cognitive functions, impacting daily life. Alzheimer's
disease (AD) is the most common cause of dementia, accounting for 50--70\% of
cases, while frontotemporal dementia (FTD) affects social skills and
personality. Electroencephalography (EEG) provides an effective tool to study
the effects of AD on the brain. {Methods: } In this study, we propose to use
shallow neural networks applied to two sets of features: spectral-temporal and
functional connectivity using four methods. We compare three supervised machine
learning techniques to the CNN models to classify EEG signals of AD / FTD and
control cases. We also evaluate different measures of functional connectivity
from common EEG frequency bands considering multiple thresholds. {Results and
Discussion: } Results showed that the shallow CNN-based models achieved the
highest accuracy of 94.54\% with AEC in test dataset when considering all
connections, outperforming conventional methods and providing potentially an
additional early dementia diagnosis tool.
\url{https://doi.org/10.3389%2Ffneur.2023.1270405}
