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Related Experiment Video

Updated: Jun 3, 2025

Examining the Characteristics of Episodic Memory using Event-related Potentials in Patients with Alzheimer's Disease
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Exploring Task-Related EEG for Cross-Subject Early Alzheimer's Disease Susceptibility Prediction in Middle-Aged

Ziyang Li1, Hong Wang1, Jianing Song1

  • 1Department of Mechanical Engineering and Automation, Northeastern University, Wenhua Street, Shenyang 110819, China.

Sensors (Basel, Switzerland)
|January 11, 2025
PubMed
Summary

Early Alzheimer

Keywords:
AD detectioncross-subjectmachine learningmultitapertask-state EEG

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Area of Science:

  • Neuroscience
  • Biomedical Engineering

Background:

  • Early prediction of Alzheimer's disease (AD) risk is crucial but challenging.
  • Resting-state electroencephalogram (EEG) has limitations for accurate risk assessment.

Purpose of the Study:

  • To investigate the feasibility of using task-state EEG signals for improved Alzheimer's disease risk detection.
  • To identify specific EEG features and tasks predictive of AD risk.

Main Methods:

  • Collected EEG data during the Multi-Source Interference Task (MSIT) and Sternberg Memory Task (STMT).
  • Extracted time-frequency features using the Multitaper method and applied dimensionality reduction.
  • Selected subspace features (F24, F216) and analyzed them using TFAAT and PBTST.

Main Results:

  • The MSIT task with the TFAAT feature set achieved 58% ROC AUC using Support Vector Machine (SVM).
  • The STMT task with the PBTST feature set showed classification ability using logistic regression.
  • Prefrontal beta band power spectrum emerged as a potential AD risk marker.

Conclusions:

  • Task-state EEG signals demonstrate superior classification potential over resting-state EEG for AD risk prediction.
  • Specific cognitive tasks and EEG features can enhance early detection accuracy.
  • Findings support the development of novel EEG-based biomarkers for Alzheimer's disease.