Dementia in the elderly has recently become the most usual cause of cognitive
decline. The proliferation of dementia cases in aging societies creates a
remarkable economic as well as medical problems in many communities worldwide.
A recently published report by The World Health Organization (WHO) estimates
that about 47 million people are suffering from dementia-related neurocognitive
declines worldwide. The number of dementia cases is predicted by 2050 to
triple, which requires the creation of an AI-based technology application to
support interventions with early screening for subsequent mental wellbeing
checking as well as preservation with digital-pharma (the so-called beyond a
pill) therapeutical approaches. We present an attempt and exploratory results
of brain signal (EEG) classification to establish digital biomarkers for
dementia stage elucidation. We discuss a comparison of various machine learning
approaches for automatic event-related potentials (ERPs) classification of a
high and low task-load sound stimulus recognition. These ERPs are similar to
those in dementia. The proposed winning method using tensor-based machine
learning in a deep fully connected neural network setting is a step forward to
develop AI-based approaches for a subsequent application for subjective- and
mild-cognitive impairment (SCI and MCI) diagnostics.