1 research outputs found
Brain correlates of task-load and dementia elucidation with tensor machine learning using oddball BCI paradigm
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.Comment: In ICASSP 2019 - 2019 IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP), pp. 8578-8582, May 201