Engineering, 2013, 5, 53-57
doi:10.4236/eng.2013.55B011 Published Online May 2013 (
Monitoring Mental Fatigue in Analog Space Environment
Using Optical Brain Imaging
Xuejun Jiao1,2, Jing Bai1, Shanguan g Chen2, Qijie Li2
1Tsinghua University, Beijing, China
2National Laboratory of Human Factors Engineering, China Astronaut Research and Training Centre, Beijing, China
Received 2013
Accurate assessment of mental fatigue level would improve operational safety and efficacy of astronauts for long-term
space flight. Identification of neurophysiological markers can index impending overload or fatigue before performance
decrements using neuroimaging technologies. The current study utilized functional near-infrared spectroscopy (fNIR) to
investigate the relationship of hemodynamic response in prefrontal cortex with changes of mental fatigue level, task
performance (reaction time) during n-back working memory task and routine work task in analog space environment.
Results indicated that the information entropy of hemodynamic response is related to task performance and subjective
self-reported measures; the reaction time is predicted by regression analysis; and the accuracy of mental fatigue
classification approaches 90%. Since fNIR is a portable, wearable and minimally intrusive methodology, it has the
potential to be deployed in future space environments to monitoring mental fatigue and assessing the effort of operators
in field environments.
Keywords: Mental Fatigue; Information Entropy; Modeling; fNIR
1. Introduction
Mental fatigue refers to changes in the psycho-physio-
logical state that people experience during and following
the course of prolonged periods of demanding cognitive
activity that require sustained mental efficiency. In other
words, mental fatigue is limited solely to a mental state
arising from a behavioral situation that includes a long-
term continuous, repetitive performance of some mental
Living in space is accompanied by a number of stress-
ors, which can be classified as physiological stressors
(e.g. microgravity, missing sunlight), psychological
stressors (e.g. isolation, confinement) and task stressors
(e.g. work overload, task stress). Astronauts are prone to
tired at long flight. Mental fatigue can cause negative
impact on work performance, alertness, cognitive ability
and emotion, even impair mission success and mission
safety during longer inhabitation of space, so mental fa-
tigue detection and countermeasure are significant to
manned space flight.
Previous studies have aimed to find the sensitive indi-
ces for evaluating mental fatigue based on performance
and perceptual, electrophysiological, psychological and
self-report measurements. Self-report and performance
techniques cannot track the dynamically changing state
of fatigue without confounding or compromising task
performance. The current tendency in ergonomic re-
search is to choose physiological measures (e.g. brain
activity, heart rate variability, galvanic skin response)to
assess mental fatigue state.
fNIR is a non-invasive, non-ionizing, real-time moni-
toring method used to determine oxygenation and
hemodynamic in tissue [1]. fNIR is sensitive to changes
in tissue oxygenation, both at the level of small blood
vessels and capillaries and at the intracellular sites of
oxygen uptake [2]. Owing to its ability to provide good
temporal and spatial resolution of oxygen availability,
this methodology may provide insights into mechanisms
regulating regional tissue blood flow and metabolism
during the performance of various tasks. Hemodynamic
indexes are quit suitable for space environment because
it can measure brain hemodynamic activity related to
human brain activity with minimally intrusive, including
sensorimotor, visual, auditory, and cognitive and lan-
guage activity noninvasively [3], as well as measures of
emotion and stress. Furthermore the instrumentation
based on continuous wave (cw) technique is quit com-
pact, so it can be made portable. The drawback of this
method is that it can only measure hemodynamic
changes which limit its applications, especially during
long term measurement. So parameters which are not
relevant to baseline need to be found.
Copyright © 2013 SciRes. ENG
Furthermore, the test procedure in space environment
should be simple, fast and comfortable. The aim of the
current study is to examine whether cw-fNIR technique
can be adopted in space environment and hemodynamic
indexes related to mental fatigue.
2. Method
We imitate blood redistribution in space environment by
-6° head down tilt bed rest, adopt n-back work memory
to induce fatigue, establish information entropy via
regression analysis of power spectrum entropy and
sample entropy with reaction time, use the changes of
information entropy to establish mental fatigue detection
model. As a comparison, we measured the change of
amplitude of Hbo at the same time. cw-fNIR apparatus
was used to record data which were sampled at 5 Hz. As
the frontal lobe area of the brain is involved with several
important activities including motor function, problem
solving, memory, language, judgment, impulse control,
and social behavior, only the data of frontal lobe were
recorded. Since the fNIR device we employed is based
on con- tinuous wave technique, it can only measure the
changes of Hbo, Hb and central blood volume (CBV).
The changes of Hbo amplitude cannot be used to detect
fa- tigue in space environment because the baseline of
brain activation is needed. Finally the mental fatigue
detection model was validated by imitating space task,
and the prediction result was estimated by regression
analysis and neural network modeling.
3. Experiment
Experiments were carried out with seventy voluntary
healthy subjects (21 - 29 years old) for n-back task to
find sensitive indices, and sixty subjects for analog space
task to validate the mental fatigue detection model. All
subjects were without any neurological deficits or
medications known to affect brain functions. The two
experiments were performed in an acoustically isolated
and dimly illuminated room. All participants signed
informed consent forms prior to the study. During the
experiments, fNIR data and task status were recorded.
Before and after the experiments, self-report
questionnaires of mental fatigue states were conducted.
3.1. N-back Paradigm Mental Fatigue Induction
There are two basic mental fatigue induction models,
sleep deprivation and mental workload. The n-back task
served as a continuous stable mental workload and the
stimuli remain unchanged or change in a predictable
manner from which to establish mental fatigue induction
model. The n-back task is a well-characterized paradigm
with robust correlations between levels of difficulty and
cortical activation, including prefrontal cortex [4]. The
n-back task sequentially presents items(letters, spatial
position, or pictures) to be evaluated for their identity to
an element that was presented 0,1,2, or 3 items
previously. As such, the task requires encoding,
temporary maintenance and rehearsal, tracking or serial
order, up- dating, comparison and response processes,
functions of working memory and attention which is
closely related to the function of prefrontal lobe. In this
experiment, we selected 2-back with pictures item
because the difficulty of 3-back task exceeds the most
subject’s ability to keep up with their working memory.
Mental fatigue was induced by long term experiment
(almost 4 hours) from which to get the average entropy
change and extract in- formation entropy. Response time
and correct rate were selected as performance indices.
3.2. Monitoring and Controlling Task Mental
Fatigue Induction
In order to imitate space task closely, Monitoring and
controlling task was selected to induce mental fatigue,
which can validate the mental fatigue detection model.
Monitoring and controlling task includes 9 time variable
controlling parameters which controlled the mental
workload. Time of parameters in threshold is used as
performance index.
4. Data Sample
Simultaneous measurement of fNIR was performed 5
minutes before and throughout the experiment period.
Optrodes for producing near-infrared light (n = 9) and for
collecting transmitted light (n = 4) were placed alter-
nately at 2.5 cm intervals in a 4 *7.2 cm square on the
frontal cortex area which deals with high-level processing
[8]: working memory, planning, problem solving, mem-
ory retrieval and attention, resulting in 16 non-overlap-
ping channels, as shown in Figure 1. The probe was po-
sitioned that the base of it aligned with the eyebrows of
the subject and the middle aligned with the Fz location
from EEG electrode placement and a black sports ban-
dage was used to secure it and eliminate background
light leakage. Hence, 48 optic signals of 3 wavelengths
Figure 1. The layout of sources and detectors.
Copyright © 2013 SciRes. ENG
X. J. JIAO ET AL. 55
of sixteen channel scan be acquired in one scan of the
forehead. Prior to digitization, analog optical density
signals were filtered by an LP filter with 1 Hz cut-off
frequency. The optic attenuation signals were collected at
an acquisition rate of 5 Hz. Subjects behavioral status,
experiment status, as well as hemodynamic data, were
continuously and carefully monitored visually and re-
corded by a well-trained research attendants.
5. Data Analysis
Experimental artifacts were mainly induced by slippage
of the probes on the forehead, which is related to head
motion or facial expression, and physiological
interference which is mainly due to physiological signals
in the superficial layers and underlying cerebral tissue,
including cardiac pulsations, respiratory signals, and
blood pressure changes. The spectrum of a typical fNIR
signal has 4 bands – B waves, M waves, respiration and
arterial pulsation (heart beat). The relevant bandwidths
are [0.6-2.0] Hz for cardiac pulsations, [0.15-0.4] Hz for
respiratory artifacts, and [0.05-0.2] Hz for blood pressure
waves [6,7]. The neural response is embedded in the B
and M wave’s bands. Among the interferences,
physiological signals are valuable for mental fatigue
detection because they can reflect changes of autonomy
nerve system which is relevant to mental fatigue, so only
motion artifacts and measurement outliers are concerned
in this paper.
5.1. Preprocessing
The cognitive activity is mainly localized above 0.03 Hz,
that is, in the 0.03 - 0.3 Hz range, and the autonomic
nervous system is related to workload and mental fatigue,
so for each participant, raw fNIR data (16 optodes × 3
wavelengths) were filtered with wavelet threshold
noising method to attenuate the high frequency noise
above 1.2 Hz, the M waves, B waves, respiration and
cardiac cycle effects were reserved to reflect biological
status better. Saturated channels, in which light intensity
at the detector was higher than the analog-to-digital
converter limit, were excluded. Oxygenation changes
were calculated using the Modified Beer Lambert Law
for task periods with respect to rest periods prior to the
task. Motion artifacts were canceled using moving
standard deviation and wavelet singularity detection
5.2. Sample Entropy
Sample entropy (SE) is a complexity measurement of
time sequence. It is the negative logarithm of the
conditional probability that a point which repeats itself
within a tolerance of ε in an m dimensional phase space
will repeat itself in an m+1 dimensional phase space.
a(, )-log()
(1,Cm )
is the number of repeating points in the m
dimensional phase space. Repeating was defined as
points closer in a Euclidean sense than ε to the examined
point. Sample entropy were calculated using two
different tolerances ε = 0.2 and ε = 0.5 times the standard
deviation of the signal. To fully represent the dynamic
system, the embedding dimension, m, and embedding
delay, τ, must be proper. In the current study a systematic
approach was applied, by calculating the embedding
matrix for Sample entropy with several combinations of
embedding dimensions and embedding delays. SE was
calculated with embedding dimensions from m = 2 to 8
and embedding delays τ = 1, 3, 5, 8, 12.
5.3. Power Spectrum Entropy
Power spectrum entropy (PSE) is an uncertainty
measurement of frequency. PSE describes spectrum
structure of a time sequence. PSE was calculated as
where ()
fi denotes spectral components of initial
signal, i indicates frequency index of FFT, and N is the
length of FFT. Because the frequency band of fNIR is
between 0.06 Hz and 0.4 Hz, the frequency components
outside 0.06 Hz to 0.4 Hz are set to zero. Finally the
negative logarithm is calculated in the following:
() -log
P (3)
In order to avoid the leaps of power spectrum due to
interference, the smooth algorithm is adopted. The
smooth method is as follows:
()(-1)( -1)()
xHx H
 x (4)
where ()
x is PSE, ()
x is PSE after smoothing,
is smoothing factor which is defined according to
stability of the signal range from 0.9 to 0.95.
5.4. Information Entropy
In order to calculate information entropy (IE), the weight
coefficients of PSE and SE need to be confirmed.
Regression equation of PSE and SE of n-back
experiment with reaction time (RT) was established
using spss17.0 statistics software. The regression
equation is as follows:
Copyright © 2013 SciRes. ENG
RT=-1.767+0.297*SE+0.219*PSE (5)
where RT is reaction time, SE is mean of sample entropy;
PSE is mean of power spectrum entropy.To some extent,
RT reflects mental fatigue state, so the weight
coefficients of information entropy relevant to mental
fatigue are 0.297 for SE and 0.219 for PSE. Thus, the
information entropy can be calculated with equation (6).
IE=-1.767+0.297*SE+0.219*PSE (6)
where IE is information entropy.
6. Results
All data was processed with ambient light interferences
restrain, motion artifacts cancellation and wavelet based
high frequent noises reduction. The results of motion
artifacts cancellation is shown in Fi gu re 2.
It is obvious that large motion artifacts were cancelled.
Before processing, the amplitude of Hbo in the forth
n-back experiment is rising, whereas the operator was
very tired. After processing, the amplitude of Hbo is
declining in fatigue state which is corresponding with the
former research [5].
6.1. N-back Experiments
The performance data of n-back experiments were
processed with SPSS17.0. The response time of first
n-back is 481.174 ± 16.615 ms with the correct rate 89.133
± 3.246%, while the response time of last n-back is
695.133 ± 37.0571ms with the correct rate 77.277 ±
1.6464%. The results indicate that tedious mental task
can result in mental fatigue and the cognitive functions of
operators were damaged, as a result, the average
response time increases and the average correct rate
decreases. RT is critical to space task with time limited,
RT prediction of n-back task was conducted with the
regression equation. The prediction result is shown in
Figure 3.
After the data preprocessed, sample entropies and
power spectrum entropies of every subjects were
calculated. The statistic results show that the SE and PSE
during mental fatigue state decline greatly. SE declines
54.09 ± 2.71% and PSE declines76.28 ± 4.45%. The
results are shown in Table 1.In order to classify mental
fatigue, classification threshold was determined as follows:
Threshold=ε*(-1.767+0.297*54.09+0.219*76.28) (7)
ε is the coefficient verified on the basis of experiment
6.2. Routine Work Experiments
In order to validate the methodology, the routine work
experiments were conducted to imitate space tasks.
Among 58 subjects (2 subjects were excluded because
data sampled was contaminated by MAs), 23 subjects are
in mental fatigue status after 6 hours routine work. IE of
every subject is calculated, the status of subjects are
classified with the statistic threshold of n-back
experiments and neural network.
Figure 2. Results of motion artifacts cancellation.
Figure 3. Result of prediction RT comparing to the actual
Table 1. Entropies data of fNIR.
NumB_p E_p B_s E_s C_PE C_SEC_IE
1 9.76732.08320.31620.1661 78.67 47.5063.09
2 10.51132.32920.35710.1803 77.84 49.5163.68
3 10.50432.81580.37820.1631 73.94 56.8765.41
4 9.99082.88980.40720.1702 71.08 58.2064.64
5 10.43361.07150.30700.1804 89.75 41.2465.50
6 8.26251.02330.34560.1937 83.93 43.9568.94
7 8.66491.94230.39910.1521 77.58 61.8979.74
8 3.74351.60340.45590.1166 57.17 74.4264.80
9 9.83331.50060.31240.1843 84.74 41.0172.18
1010.52561.25320.38120.1667 88.09 56.2772.18
11 9.89252.10910.40690.1519 77.85 62.6770.26
1210.70184.84240.36890.1641 54.75 55.5255.14
mean 76.28 54.0965.52
6.2.1. Results of Regr ession Anal ys i s Classi fication
Taking into account IE data and subject status, the error
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detection rate and the missing detection rate reaches a
balance when ε is 0.8. The result is shown in Figure 4.
The accuracy of regression analysis classification is
87.93%, the number of error detection is 3, and the
missing detection is 4.
Figure 4. Result of regression classification, the line
represents the threshold, the rectangles represent mental
fatigue state, the circles present non-mental fatigue.
6.2.2. Results of Neur a l N etwork Classification
Though regression analysis method can classify mental
fatigue state and predict RT, it needs all experiment data
to determine ε. In order to classify mental fatigue state in
real time, we adopted neural network classification. The
input layer includes 2 neural cells, SE and PSE. The
output layer includes 2 states, mental fatigue and non-
mental fatigue. The number of hidden layer neural cell is
defined by the empirical equation:
 (8)
where n is the neural cell number of hidden layer, l is the
neural cell number of input layer, m is the neural cell
number of output layer. 70% data was randomly selected
as train set, the remainder as test set. We could
distinguish between mental fatigue and non-mental
fatigue state with 88.2% accuracy using neural network
7. Discussion and Conclusions
In this study, a physiological method was employed to
measure mental fatigue during a simulated space task.
For all 58 subjects, the relative change of IE showed
statistical significance (p < 0.01) before and after
long-term task. It is found that the decreasing IE,
indicates the declining arousal level when mental fatigue
The mental fatigue detection methodology based on
cw-fNIR, which adopts IE as feature parameter other
than concentrates on the changes of hemodynamic
indices, can overcome the disadvantages of cw-fNIR,
improve the convenience of measurement and contract
the measurement period. Both statistic analysis and
neural network classification were used to investigate the
data; results indicate that mental fatigue can be reliably
and noninvasively detected by applying IE of fNIR.
Since cw-fNIR technology allows the development of
mobile, non-intrusive and miniaturized devices, and
through the aforementioned applications it is remarked
that IE can be considered as a very promising and useful
solution avoiding the disadvantages of cw-fNIR, it has
the potential to be deployed to monitoring fatigue status
of astronaut under ambulant conditions in space
environment in the future.
8. Acknowledgements
This research is supported by National Basic Research
Program of China (973 Program) (NO.2011CB711000).
[1] Van Beekvelt, M. C. Colier, W. N. Wevers, R. A. Van
and B. G. Engelen, “Performance of Near-Infrared
Spectroscopy in Measuring Local O2 Consumption and
Blood Flow in Skeletal Muscle,” Journal of Applied
Physiology, Vol. 90, No. 2, 2001, pp.511-519.
[2] A. Villringer and B. Chance, “Non-Invasive Optical
Spectroscopy and Imaging of Human Brain Function,”
Trends in Neuroscience, Vol. 20, No. 10, 1997, pp.
435-442. doi:10.1016/S0166-2236(97)01132-6
[3] Hasan Ayaza, P. A. Shewokis et al., “Optical Brain
Monitoring for Operator Training and Mental Workload
Assessment,” NeuroImage, Vol. 59, 2012, pp. 36-47.
[4] C. Zhang, C. X. Zheng, X. L. Yu and Y. Ouyang,
“Estimating VDT Mental Fatigue Using Multichannel
Linear Descriptors and KPCA-HMM,” EURASIP Journal
on Advances in Signal Processing, Vol. 2008, No. 1,
[5] J. Wu and D. T. Ye. “Evaluation of Anti-Fatigue Effect of
Health Protection Food with fNIR,” Spectroscopy and
Spectral Analysis, Vol. 29, No. 9, pp. 2357-2360.
[6] Y. Son and B. Yazıcı, “Near Infrared Imaging and
Spectroscopy for Brain Activity Monitoring,” Advances
in Sensing with Security Applications, Vol. 2, 2006, pp
341-372. doi:10.1007/1-4020-4295-7_15
[7] Ajit. Devaraj, “Signal Processing for Functional
Near-Infrared Neuroimaging,” Drexel Theses and
Dissertations, 2005.
[8] Ramnani, N. and Owen, A.M. “Anterior Prefrontal Cortex:
Insights into Function from Anatomy and
Neuroimaging,” Nat Rev Neurosci, Vol. 5, No. 3, 2004,
pp. 184-194. doi:10.1038/nrn1343