Annals of Biomedical Engineering

, Volume 42, Issue 10, pp 2084–2094 | Cite as

Discriminative Analysis of Brain Functional Connectivity Patterns for Mental Fatigue Classification

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


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.


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



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.

Supplementary material

10439_2014_1059_MOESM1_ESM.tif (3.1 mb)
Figure S1. The cortical regions of interest (ROIs) were overlaid on the brain surface at the axial view. The 26 ROIs (13 in each hemisphere), including 4 frontal regions, 16 motor related regions, 4 somatosensory regions, and 2 associative visual regions. For better visualization purposes, labels of ROIs in the left hemisphere were listed in the figure. BA = Brodmann area, L = left, and R = right. (TIFF 3216 kb)
10439_2014_1059_MOESM2_ESM.tif (6.8 mb)
Figure S2. Group average cortical connectivity patterns for (a) rested state and (b) fatigued state. The brain is seen from above. Twenty-six ROIs were labeled and the cortical connections are coded with lines and arrows. The width and color of lines indicates the strengths of the connections. The top 5% cortical connections shared by at least five subjects are shown for illustration. The figure was visualized with BrainNet Viewer software ( (TIFF 6991 kb)
10439_2014_1059_MOESM3_ESM.pdf (47 kb)
Figure S3. Flow chart of the supervised learning approach used in the current work. (PDF 46 kb)


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Copyright information

© Biomedical Engineering Society 2014

Authors and Affiliations

  • Yu Sun
    • 1
  • Julian Lim
    • 1
    • 2
    • 3
  • Jianjun Meng
    • 4
  • Kenneth Kwok
    • 1
    • 2
  • Nitish Thakor
    • 1
  • Anastasios Bezerianos
    • 1
  1. 1.Singapore Institute for Neurotechnology (SINAPSE), Centre for Life SciencesNational University of SingaporeSingaporeSingapore
  2. 2.Temasek LaboratoriesNational University of SingaporeSingaporeSingapore
  3. 3.Department of PsychologyNational University of SingaporeSingaporeSingapore
  4. 4.BioMechatronics and BioRobotics Laboratory, School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghaiChina

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