TITLE:
EEG-Based Estimation and Classification of Mental Fatigue
AUTHORS:
Leonard J. Trejo, Karla Kubitz, Roman Rosipal, Rebekah L. Kochavi, Leslie D. Montgomery
KEYWORDS:
EEG, Mental Fatigue, Alertness, Drowsiness
JOURNAL NAME:
Psychology,
Vol.6 No.5,
April
9,
2015
ABSTRACT: Mental fatigue
was associated with increased power in frontal theta (θ) and parietal alpha (α)
EEG rhythms. A statistical classifier can use these effects to model EEG-fatigue
relationships accurately. Participants (n = 22) solved math problems on a computer
until either they felt exhausted or 3 h had elapsed. Pre- and post-task mood scales
showed that fatigue increased and energy decreased. Mean response times rose from
6.7 s to 7.9 s but accuracy did not change significantly. Mean power spectral densities
or PSDs of θ and α bands rose by 29% and 44%, respectively. A kernel partial least squares
classifier trained to classify PSD coefficients (1 - 18 Hz) of single 13-s EEG segments
from alert or fatigued task periods was 91% to 100% accurate. For EEG segments from
other task periods, the classifier outputs tracked the time course of the development
of mental fatigue. By this measure, most subjects became substantially fatigued
after 60 min of task performance. However, the trajectories of individual classifier
outputs showed that EEG signs of developing fatigue were present in all subjects
after 15 - 30 minutes of task performance. The results show that EEG can track the
development of mental fatigue over time with accurate updates on a time scale a
short as 13 seconds. In addition, the results agree with the notion that growing
mental fatigue produces a shift away from executive and attention networks to default
mode and is accompanied by a shift in alpha frequency to the lower alpha band.