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Article

A Novel Working Memory Task-Induced EEG Response (WM-TIER) Feature Extraction Framework for Detecting Alzheimer’s Disease and Mild Cognitive Impairment

by
Yi-Hung Liu
1,†,
Thanh-Tung Trinh
2,3,†,
Chia-Fen Tsai
4,5,
Jie-Kai Yang
1,
Chun-Ying Lee
2,6 and
Chien-Te Wu
7,8,*
1
Institute of Electrical and Control Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
2
Graduate Institute of Manufacturing Technology, College of Mechanical and Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
3
Department of Information and Communication Technology, Hanoi School of Business and Management, Vietnam National University, Hanoi 100000, Vietnam
4
Department of Psychiatry, Division of Geriatric Psychiatry, Taipei Veterans General Hospital, Taipei 11217, Taiwan
5
Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
6
Department of Mechanical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
7
Department of Occupational Therapy, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32608, USA
8
Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL 32608, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Biosensors 2025, 15(5), 289; https://doi.org/10.3390/bios15050289
Submission received: 8 March 2025 / Revised: 28 April 2025 / Accepted: 29 April 2025 / Published: 4 May 2025
(This article belongs to the Section Biosensors and Healthcare)

Abstract

The electroencephalography (EEG)-based approach provides a promising low-cost and non-invasive approach to the early detection of pathological cognitive decline. However, current studies predominantly utilize EEGs from resting state (rsEEG) or task-state (task EEG), posing challenges to classification performances due to the unconstrainted nature of mind wandering during resting state or the inherent inter-participant variability from task execution. To address these limitations, this study proposes a novel feature extraction framework, working memory task-induced EEG response (WM-TIER), which adjusts task EEG features by rsEEG features and leverages the often-overlooked inter-state changes of EEGs. We recorded EEGs from 21 AD individuals, 24 MCI individuals, and 27 healthy controls (HC) during both resting and working memory task conditions. We then compared the classification performance of WM-TIER to the conventional rsEEG or task EEG framework. For each framework, three feature types were examined: relative power, spectral coherence, and filter-bank phase lag index (FB-PLI). Our results indicated that FB-PLI-based WM-TIER features provide (1) better AD/MCI versus HC classification accuracy than rsEEG and task EEG frameworks and (2) high accuracy for three-class classification of AD vs. MCI vs. HC. These findings suggest that the EEG-based rest-to-task state transition can be an effective neural marker for the early detection of pathological cognitive decline.
Keywords: electroencephalography; Alzheimer’s disease; mild cognitive impairment; machine learning; working memory task-induced EEG response electroencephalography; Alzheimer’s disease; mild cognitive impairment; machine learning; working memory task-induced EEG response

Share and Cite

MDPI and ACS Style

Liu, Y.-H.; Trinh, T.-T.; Tsai, C.-F.; Yang, J.-K.; Lee, C.-Y.; Wu, C.-T. A Novel Working Memory Task-Induced EEG Response (WM-TIER) Feature Extraction Framework for Detecting Alzheimer’s Disease and Mild Cognitive Impairment. Biosensors 2025, 15, 289. https://doi.org/10.3390/bios15050289

AMA Style

Liu Y-H, Trinh T-T, Tsai C-F, Yang J-K, Lee C-Y, Wu C-T. A Novel Working Memory Task-Induced EEG Response (WM-TIER) Feature Extraction Framework for Detecting Alzheimer’s Disease and Mild Cognitive Impairment. Biosensors. 2025; 15(5):289. https://doi.org/10.3390/bios15050289

Chicago/Turabian Style

Liu, Yi-Hung, Thanh-Tung Trinh, Chia-Fen Tsai, Jie-Kai Yang, Chun-Ying Lee, and Chien-Te Wu. 2025. "A Novel Working Memory Task-Induced EEG Response (WM-TIER) Feature Extraction Framework for Detecting Alzheimer’s Disease and Mild Cognitive Impairment" Biosensors 15, no. 5: 289. https://doi.org/10.3390/bios15050289

APA Style

Liu, Y.-H., Trinh, T.-T., Tsai, C.-F., Yang, J.-K., Lee, C.-Y., & Wu, C.-T. (2025). A Novel Working Memory Task-Induced EEG Response (WM-TIER) Feature Extraction Framework for Detecting Alzheimer’s Disease and Mild Cognitive Impairment. Biosensors, 15(5), 289. https://doi.org/10.3390/bios15050289

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