Identifying Individuals With Mild Cognitive Impairment Using Working Memory-Induced Intra-Subject Variability of Resting-State EEGs

Thanh-Tung Trinh; Chia-Fen Tsai; Yu-Tsung Hsiao; Chun-Ying Lee; Chien-Te Wu; Yi-Hung Liu

doi:10.3389/fncom.2021.700467

Identifying Individuals With Mild Cognitive Impairment Using Working Memory-Induced Intra-Subject Variability of Resting-State EEGs

Front Comput Neurosci. 2021 Aug 4:15:700467. doi: 10.3389/fncom.2021.700467. eCollection 2021.

Authors

Thanh-Tung Trinh¹, Chia-Fen Tsai^{2

3}, Yu-Tsung Hsiao⁴, Chun-Ying Lee⁵, Chien-Te Wu⁶, Yi-Hung Liu⁷

Affiliations

¹ Neural Engineering and Smart Systems Laboratory, Graduate Institute of Manufacturing Technology, College of Mechanical and Electrical Engineering, National Taipei University of Technology (Taipei Tech), Taipei, Taiwan.
² Department of Psychiatry, Division of Geriatric Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan.
³ Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
⁴ Neural Engineering and Smart Systems Laboratory, Graduate Institute of Mechatronic Engineering, National Taipei University of Technology (Taipei Tech), Taipei, Taiwan.
⁵ Department of Mechanical Engineering, National Taipei University of Technology (Taipei Tech), Taipei, Taiwan.
⁶ International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan.
⁷ Department of Mechanical Engineering, National Taiwan University of Science and Technology (Taiwan Tech), Taipei, Taiwan.

Abstract

Individuals with mild cognitive impairment (MCI) are at high risk of developing into dementia (e. g., Alzheimer's disease, AD). A reliable and effective approach for early detection of MCI has become a critical challenge. Although compared with other costly or risky lab tests, electroencephalogram (EEG) seems to be an ideal alternative measure for early detection of MCI, searching for valid EEG features for classification between healthy controls (HCs) and individuals with MCI remains to be largely unexplored. Here, we design a novel feature extraction framework and propose that the spectral-power-based task-induced intra-subject variability extracted by this framework can be an encouraging candidate EEG feature for the early detection of MCI. In this framework, we extracted the task-induced intra-subject spectral power variability of resting-state EEGs (as measured by a between-run similarity) before and after participants performing cognitively exhausted working memory tasks as the candidate feature. The results from 74 participants (23 individuals with AD, 24 individuals with MCI, 27 HC) showed that the between-run similarity over the frontal and central scalp regions in the HC group is higher than that in the AD or MCI group. Furthermore, using a feature selection scheme and a support vector machine (SVM) classifier, the between-run similarity showed encouraging leave-one-participant-out cross-validation (LOPO-CV) classification performance for the classification between the MCI and HC (80.39%) groups and between the AD vs. HC groups (78%), and its classification performance is superior to other widely-used features such as spectral powers, coherence, and the complexity estimated by Katz's method extracted from single-run resting-state EEGs (a common approach in previous studies). The results based on LOPO-CV, therefore, suggest that the spectral-power-based task-induced intra-subject EEG variability extracted by the proposed feature extraction framework has the potential to serve as a neurophysiological feature for the early detection of MCI in individuals.

Keywords: Alzheimer's disease; between-run similarity; brain-computer interface; electroencephalography; intra-subject variability; machine learning; mild cognitive impairment.