  Dementia and especially Alzheimer's disease (AD) are the most common causes
of cognitive decline in elderly people. A spread of the above mentioned mental
health problems in aging societies is causing a significant medical and
economic burden in many countries around the world. According to a recent World
Health Organization (WHO) report, it is approximated that currently, worldwide,
about 47 million people live with a dementia spectrum of neurocognitive
disorders. This number is expected to triple by 2050, which calls for possible
application of AI-based technologies to support an early screening for
preventive interventions and a subsequent mental wellbeing monitoring as well
as maintenance with so-called digital-pharma or beyond a pill therapeutical
approaches. This paper discusses our attempt and preliminary results of
brainwave (EEG) techniques to develop digital biomarkers for dementia progress
detection and monitoring. We present an information geometry-based
classification approach for automatic EEG-derived event related responses
(ERPs) discrimination of low versus high task-load auditory or tactile stimuli
recognition, of which amplitude and latency variabilities are similar to those
in dementia. The discussed approach is a step forward to develop AI, and
especially machine learning (ML) approaches, for the subsequent application to
mild-cognitive impairment (MCI) and AD diagnostics.
