On the challenges of detecting MCI using EEG in the wild
Journal:
arXiv
Published Date:
Jan 15, 2025
Abstract
Recent studies have shown promising results in the detection of Mild
Cognitive Impairment (MCI) using easily accessible Electroencephalogram (EEG)
data which would help administer early and effective treatment for dementia
patients. However, the reliability and practicality of such systems remains
unclear. In this work, we investigate the potential limitations and challenges
in developing a robust MCI detection method using two contrasting datasets: 1)
CAUEEG, collected and annotated by expert neurologists in controlled settings
and 2) GENEEG, a new dataset collected and annotated in general practice
clinics, a setting where routine MCI diagnoses are typically made. We find that
training on small datasets, as is done by most previous works, tends to produce
high variance models that make overconfident predictions, and are unreliable in
practice. Additionally, distribution shifts between datasets make cross-domain
generalization challenging. Finally, we show that MCI detection using EEG may
suffer from fundamental limitations because of the overlapping nature of
feature distributions with control groups. We call for more effort in
high-quality data collection in actionable settings (like general practice
clinics) to make progress towards this salient goal of non-invasive MCI
detection.
