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Related Experiment Video

Updated: Oct 10, 2025

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
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An explainable Artificial Intelligence approach to study MCI to AD conversion via HD-EEG processing.

Francesco Carlo Morabito1, Cosimo Ieracitano1, Nadia Mammone1

  • 1DICEAM, 19009University Mediterranea of Reggio Calabria, Via Graziella Feo di Vito, 89124, Reggio Calabria, Italy.

Clinical EEG and Neuroscience
|December 10, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an explainable AI (xAI) method using high-density electroencephalography (HD-EEG) to monitor Mild Cognitive Impairment (MCI) progression to Alzheimer's Disease (AD). The xAI approach accurately identifies individual changes in brain activity, aiding in early detection of neurodegeneration.

Keywords:
Alzheimer’s DiseaseConvolutional Neural NetworkHigh-Density ElectroencephalographyMild Cognitive Impairmentexplainable Artificial Intelligence

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Mild Cognitive Impairment (MCI) is a transitional stage between normal aging and Alzheimer's Disease (AD).
  • Longitudinal monitoring of MCI patients is crucial for understanding disease progression and enabling timely intervention.
  • High-density electroencephalography (HD-EEG) offers a non-invasive method for capturing detailed brain activity.

Purpose of the Study:

  • To develop and validate an explainable Artificial Intelligence (xAI) approach for monitoring the progression of Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD).
  • To utilize high-density electroencephalography (HD-EEG) data to detect individual changes in brain activity associated with neurodegeneration.
  • To identify specific brain regions and frequency bands critical for distinguishing MCI from AD using xAI.

Main Methods:

  • Mapping segments of HD-EEG data into channel-frequency maps using power spectral density.
  • Inputting these maps into a Convolutional Neural Network (CNN) trained to classify states as "T0" (MCI) or "T1" (AD).
  • Employing a Grad-CAM approach to explore the explainability of the CNN and identify salient EEG features.

Main Results:

  • The CNN achieved high intra-subject classification performance, with an accuracy rate up to 98.97%.
  • Explainability analysis revealed that the delta sub-band of EEG signals contains critical information for detecting progression to AD.
  • Specific head regions, including the left-temporal, central-frontal, parietal, and left-frontal lobes, were identified as highly relevant areas.

Conclusions:

  • The proposed xAI methodology effectively monitors MCI progression to AD using HD-EEG.
  • The delta sub-band and specific brain regions are key indicators of neurodegenerative changes from MCI to AD.
  • This approach holds promise for early detection and personalized monitoring of Alzheimer's Disease progression.