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Updated: Feb 2, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Non-Invasive, Cost-Effective, Early Diagnosis of Mild Cognitive Impairment in an Outpatient Setting: Pilot Study
Abstract:
Mild cognitive impairment (MCI) and Alzheimer's Disease (AD) affect millions worldwide, yet no curative treatments for these neuro-degenerative disorders have been developed to date. The current study aims to propose a noninvasive, cost-effective, early diagnostic protocol for individuals suffering with MCI in an outpatient setting. Elderly participants (n=11) were screened for MCI utilizing the Montreal Cognitive Assessment (MoCA) questionnaire preceding a visual stimuli task. Participants were presented with facial stimuli to elicit event related potentials (ERP) while their cortical activity was recorded utilizing electroencephalogram (EEG). Combining regional neurophysiological biomarkers into a multidimensional feature space allowed for differentiation between healthy and MCI participants based on their respective MoCA scores. This study illustrates the feasibility of recording reliable EEG in an outpatient setting while presenting a novel method for diagnosing MCI in elderly (age >60) populations.
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