Discriminative Analysis of Brain Functional Connectivity Patterns for Mental Fatigue Classification

Yu Sun, Julian Lim, Jianjun Meng, Kenneth Kwok, Nitish Thakor, Anastasios Bezerianos

Research output: Contribution to journalArticlepeer-review

32 Scopus citations

Abstract

Mental fatigue is a commonly experienced state that can be induced by placing heavy demands on cognitive systems. This often leads to lowered productivity and increased safety risks. In this study, we developed a functional-connectivity based mental fatigue monitoring method. Twenty-six subjects underwent a 20-min mentally demanding test of sustained attention with high-resolution EEG monitoring. Functional connectivity patterns were obtained on the cortical surface via source localization of cortical activities in the first and last 5-min quartiles of the experiment. Multivariate pattern analysis was then adopted to extract the highly discriminative functional connectivity information. The algorithm used in the present study demonstrated an overall accuracy of 81.5% (p < 0.0001) for fatigue classification through leave-one-out cross validation. Moreover, we found that the most discriminative connectivity features were located in or across middle frontal gyrus and several motor areas, in agreement with the important role that these cortical regions play in the maintenance of sustained attention. This work therefore demonstrates the feasibility of a functional-connectivity-based mental fatigue assessment method, opening up a new avenue for modeling natural brain dynamics under different mental states. Our method has potential applications in several domains, including traffic and industrial safety.

Original languageEnglish (US)
Pages (from-to)2084-2094
Number of pages11
JournalAnnals of Biomedical Engineering
Volume42
Issue number10
DOIs
StatePublished - Oct 2014

Bibliographical note

Funding Information:
The authors thank the National University of Singapore for supporting the Cognitive Engineering Group at the Singapore Institute for Neurotechnology (SINAPSE) under Grant Number R-719-001-102-232. This study was also supported by the NEUROEN Grant R3940000059232. We acknowledge the assistance of Sheralyn Tan and Ong How Hwee in EEG data collection and Jie Fu in data processing.

Publisher Copyright:
© 2014, Biomedical Engineering Society.

Keywords

  • Cross-validation
  • Electroencephalography (EEG)
  • Multivariate pattern analysis (MVPA)
  • Partial directed coherence (PDC)
  • Permutation
  • Psychomotor vigilance test (PVT)

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