  Dementia is one of the main causes of cognitive decline. Since the majority
of dementia patients cannot be cured, being able to diagnose them before the
onset of the symptoms can prevent the rapid progression of the cognitive
impairment. This study aims to investigate the difference in the
Electroencephalograph (EEG) signals of three groups of subjects: Normal Control
(NC), Mild Cognitive Impairment (MCI), and Dementia (DEM). Unlike previous
works that focus on the diagnosis of Alzheimer's disease (AD) from EEG signals,
we study the detection of dementia to generalize the classification models to
other types of dementia. We have developed a pilot study on machine
learning-based dementia diagnosis using EEG signals from four visual
stimulation tasks (Fixation, Mental Imagery, Symbol Recognition, and Visually
Evoked Related Potential) to identify the most suitable task and method to
detect dementia using EEG signals. We extracted both frequency and time domain
features from the EEG signals and applied a Support Vector Machine (SVM) for
each domain to classify the patients using those extracted features.
Additionally, we study the feasibility of the Filter Bank Common Spatial
Pattern (FBCSP) algorithm to extract features from the frequency domain to
detect dementia. The evaluation of the model shows that the tasks that test the
working memory are the most appropriate to detect dementia using EEG signals in
both time and frequency domain analysis. However, the best results in both
domains are obtained by combining features of all four cognitive tasks.
