EFFECTIVE INDICES FOR MONITORING MENTAL WORKLOAD

ISSN 0031-5125DOI 10.2466/22.PMS.121c12x5
Perceptual & Motor Skills: Learning & Memory
EFFECTIVE INDICES FOR MONITORING MENTAL WORKLOAD
WHILE PERFORMING MULTIPLE TASKS1
BIN-WEI HSU, MAO-JIUN J. WANG, AND CHI-YUAN CHEN
Department of Industrial Engineering and Engineering Management,
National Tsing Hua University
FANG CHEN
ATP Research Laboratory, National ICT Australia
Summary.—This study identified several physiological indices that can accu-
rately monitor mental workload while participants performed multiple tasks with
the strategy of maintaining stable performance and maximizing accuracy. Thirty
male participants completed three 10-min. simulated multitasks: MATB (Multi-
Attribute Task Battery) with three workload levels. Twenty-five commonly used
mental workload measures were collected, including heart rate, 12 HRV (heart rate
variability), 10 EEG (electroencephalography) indices (α, β, θ, α/θ, θ/β from O1-O2
and F4-C4), and two subjective measures. Analyses of index sensitivity showed
that two EEG indices, θ and α/θ (F4-C4), one time-domain HRV-SDNN (standard
deviation of inter-beat intervals), and four frequency-domain HRV: VLF (very low
frequency), LF (low frequency), %HF (percentage of high frequency), and LF/HF
were sensitive to differentiate high workload. EEG α/θ (F4-C4) and LF/HF were
most effective for monitoring high mental workload. LF/HF showed the highest
correlations with other physiological indices. EEG α/θ (F4-C4) showed strong cor-
relations with subjective measures across different mental workload levels. Opera-
tion strategy would affect the sensitivity of EEG α (F4-C4) and HF.
It is known that mental overload can cause performance degradation
and human errors, especially in multiple task situations. It is therefore im-
portant to identify valid and reliable methods for measuring mental work-
load and analyzing the mechanisms that can facilitate optimal human per-
formance and minimize human errors. In case of aviation, even a small
performance decrease may have serious consequences for safety. Humans
may have to make a greater effort to maintain a safe, stable, and accept-
able performance when the task difficulty is increased (Mascord & Heath,
1992). Thus, it is important to know if some measure can effectively pro-
vide sensitive detection of high mental workload conditions when people
try to maintain a stable performance level.
Measures of mental workload can be classified into several approach-
es including performance-based measures, subjective measures, physi-
© Perceptual & Motor Skills 20152015, 121, 1,94-117.
1
Address correspondence to Mao-Jiun J. Wang, Ph.D., Department of Industrial Engineering
and Engineering Management, National Tsing Hua University, Hsinchu 300, Taiwan, R.O.C.
or e-mail (mjwang@ie.nthu.edu.tw).
07_PMS_Hsu_150036.indd 9407_PMS_Hsu_150036.indd 94 25/08/15 4:11 PM25/08/15 4:11 PM

EFFECTIVE MENTAL WORKLOAD MEASURES 95
ological measures, and behavioral indicators (Chen, Ruiz, Choi, Epps,
Khawaja, Taib, et al., 2012). Subjective mental workload measures that
generally provide good indications of the total workload can be catego-
rized using a uni-dimensional rating, such as the Rating Scale on Mental
Effort (RSME) (De Waard, Kruizinga, A., & Brookhuis, 2008), and a multi-
dimensional rating, such as the National Aeronautics and Space Admin-
istration Task Load index (NASA-TLX). These measures are popular be-
cause of their ease of use, high face validity (Cain, 2007), and low cost
(Widyanti, Johnson, & De Waard, 2013). One particular advantage of sub-
jective measures is that they are sensitive to changes in effort, while such
effort maintains stable primary task performance (Hockey, Briner, Tatter-
sall, & Wiethoff, 1989). However, the main drawbacks of the subjective
measures are task interruption and the influence of the values by individ-
ual differences (Lin & Cai, 2009), personality characteristics, and the sub-
ject's experience (Leedal & Smith, 2005).
Using physiological measures in assessing mental workload has some
advantages over subjective measures (Miyake, Yamada, Shoji, Takae, Kuge,
& Yamamura, 2009), such as continuous and uninterrupted data recording
and real-time evaluation (Wilson, 2002). Physiological measures of men-
tal workload such as electroencephalogram (EEG), electrocardiography
(ECG) related measures, heart rate (HR), and heart rate variability (HRV)
are commonly used.
Heart rate is a practical measure because it is inexpensive, nonintru-
sive, and easy to use. It is assumed to be influenced by both the sympa-
thetic and parasympathetic autonomic nervous systems (Berntson, Bigger,
Eckberg, Grossman, Kaufmann, Malik, et al., 1997). Some studies indicat-
ed that a change in HR induced by mental task depends on the task char-
acteristics (Miyake, et al., 2009) and the perceived difficulty (Papadelis,
Kourtidou-Papadeli, Bamidis, & Albani, 2007). However, not all studies
agree with these findings. Some reports have also indicated that HR does
not provide diagnostic information about the workload (Howells, Stein,
& Russell, 2010). Roscoe (1992) indicated that HR does not appear to be
of value as a sole measure of workload but it is recommended for use as a
technique to augment a good subjective measure.
Heart rate variability is one of the most promising physiological mea-
sures of mental workload. It can be analyzed using the time or frequency
domain. Frequency domain HRV, based on the amplitudes of the cardi-
ac interval signal at various frequencies, can be classified into low, mid,
and high frequency bands. The low frequency (LF) band is treated as a
reflection of both sympathetic and parasympathetic activities with vagal
modulation. The high frequency (HF) band is treated as a reflection of
parasympathetic activity. LF/HF is usually interpreted as reflecting the

B-W. HSU, ET AL.96
sympathovagal balance (Flachenecker, Hartung, & Reiners, 1997). High
LF/HF values indicate the dominance of sympathetic activity, where-
as low LF/HF values indicate a switch toward parasympathetic activity
dominance (Cinaz, La Marca, Arnrich, & Troster, 2010). In addition, Mul-
der, Mulder, Meijman, Veldman, and Van Roon (2000) indicated that the
mid-frequency HRV is sensitive to task complexity.
Time domain HRV has been suggested for ambulatory studies as it is
less susceptible to respiratory and movement artifacts. The most frequent-
ly used indices for the time domain HRV are SDNN (standard deviation of
successive differences in normal to normal intervals), RMSSD (root mean
square of all interval differences), and pNN50 (the percentage of NN50 in-
tervals). SDNN is treated as a reflection of overall sympathetic and some
parasympathetic functions while RMSSD is treated as a reflection of the
parasympathetic function (Malik, 1996). Cinaz, et al. (2010) reported that
RMSSD and pNN50 showed a significant decrease with increased work-
load. Generally, time domain and frequency domain HRV measures pres-
ent some common limitations. The main limitation of HRV is time consid-
erations. Some spectral analysis techniques require at least 3 to 5min. of
data to correctly resolve low frequency components (Wilson, 1992).
The EEG represents the brain's electrical activity recorded from elec-
trodes placed on the scalp. It is often used to monitor the mental state of
operators by measuring brain activity during multiple tasks (Hong, Li,
Xu, Jiang, & Li, 2012). In clinical use, EEG rhythms can be divided into
four frequency ranges: delta (δ, 0.5~4Hz), theta (θ, 4~7Hz), alpha (α,
8~13Hz), and beta (β, 13~30Hz) rhythms (Fisch, 1999). The δ wave is of-
ten associated with certain encephalopathy and underlying lesions (Al-
Kadi, Ibne Reaz, & Mohd Ali, 2013). The θ wave is associated with drows-
iness, and it can be seen during hypnologic states, light sleep, and the
preconscious state just upon waking and just before falling asleep. Some
studies indicated that growing multiple task demand leads to increased
frontal θ power (Smith, Gevins, Brown, Karnik, & Du, 2001). The α wave
is associated with a relaxed, alert state of consciousness, and it attenuates
with extreme sleepiness or increased visual flow. Fournier, Wilson, and
Swain (1999) reported a decrease in the EEG α power during the execution
of multiple tasks. The β wave is often associated with busy, anxious think-
ing and active concentration (Cheng & Hsu, 2011). In addition, some stud-
ies indicated that since the power of each single EEG rhythm has a tenden-
cy to contradict the other rhythms, the EEG ratio indices were calculated
to amplify the differences (Cheng, Lee, Shu, & Hsu, 2007). For example,
EEG θ/β has been found to be strongly related to emotion (Tortella-Feliu,
et al., 2014), as well as inversely correlated with subjective attentional con-
trol and mental functioning especially in the frontal lobe (Putman, van

Peer, Maimari, & van der Werff, 2010). EEG α/θ was also reported to have
a significant correlation with lowered alertness state (Cote, Caron, Aubert,
Desrochers, & Ladouceur, 2003).
However, some EEG studies reported serious complications and in-
terferences arising from the confounding factor of overall arousal level
(Kramer, 1991). Mikhail and El-Ayat (2013) mentioned that using a large
number of electrodes to acquire EEG signals may cause serious interfer-
ence. As the number of electrodes increase, the processing time will also
increase. Thus, they suggested using 4 or 6 EEG electrodes to reach accept-
able performance. Generally, virtually all the physiological measures pres-
ent some common limitations. Respiration, interactions between physical
work, muscle activity, body position, individual difference, emotion, and
the environment might influence the results of physiological indices in
mental workload measuring (Nickel & Nachreiner, 2003).
In applied settings, one possible confounding effect in measuring
mental workload may arise from using different strategies for coping with
the tasks with increasing demands. Prior research has indicated that some
participants try to maintain acceptable performance with increasing task
demand and mental workload, while others delay low priority tasks and/
or accept lower standards of performance (Rouse, Edwards, & Hammer,
1993). Fairclough, Venables, and Tattersall (2005) postulated that stable
performance is achieved via a strategy of mental effort investment that
provokes compensatory costs of the subjective and physiological domains.
Another confounding effect may arise from different task types. It
is difficult to determine individual task demands on mental workload
among multiple tasks. Mental workload cannot be treated as a static con-
cept. In order to cope with multiple task demands, operators may change
strategies including delaying low priority tasks or lowering their perfor-
mance level (Hart, 1989). The more complex the tasks are, the more mental
activities operators perform, requiring increased mental resources and in-
creased mental workload. Since specifying which activities demand more
mental work, it is hard to evaluate the effect of each individual activity on
the total mental workload. This implies that in a complex task it is diffi-
cult to estimate the mental workload based on analyzing the performance
and characteristics of each subtask. There is no generally agreed upon pro-
cedure for combining performance scores on different subtasks into one
score that reflects the total performance.
To avoid the above confounding effects of different operation strate-
gy and multiple task performance measures, many studies still used phys-
iological measures instead of using performance measure. Gaillard and
Wientjes (1994) further specifically indicated that an increase in sympa-
thetic activity and a significant decrease in parasympathetic activity might

B-W. HSU, ET AL.98
be a representation that operators are trying to maintain adequate per-
formance. Thus, given a situation that does not permit human error, ade-
quate physiological mental workload measures under a multiple task en-
vironment are worthy of further investigation.
All of the above physiological measures have been found responding
differently to the kind of stress and activities associated with increased
mental workload. All of them have some advantages and disadvantages,
and may results in different sensitivities. This study intends to make con-
tributions in identifying physiological indices that can accurately monitor
mental workload while people perform multiple tasks under the stable
performance and minimal human error strategy.
Research goal. Physiological indices, i.e., HR, HRV, and EEG, will
be compared them with subjective measures, i.e., the NASA-
TLX and the RSME, to see which ones are effective in mon-
itoring mental workload. The physiological indices showing
significant changes (e.g., sensitivity) across a range of low, medi-
um, and high mental workload in a multiple task environment
(simulated autopilot aviation environment) will be identified,
while participants perform using a pre-specified strategy.
METHOD
Participants
Thirty randomly selected male participants with ages ranging from
20 to 25 years (M=21.8, SD=1.18) participated. They were all right-hand-
ed, with corrected vision of 20/20 and normal color vision. They were free
from cardiovascular disease, head injury, and diabetes. All were instruct-
ed not to consume any stimulants, caffeine, or alcohol for at least 24hr.
prior to each experimental session. None of them had prior experience on
the flight simulation task.
Measures
Physiological measures.—NeXus-10 (Mind Media B. V., Echt, Nether-
lands), a blue-tooth wireless physiological signal collector, was used in
conjunctionwithBioTrace+Software®(MindMediaB.V.,Roermond-Herten,
The Netherlands) to measure the participants' physiological responses.
Four sets of high-speed transmission modules were used to simultane-
ously collect ECG and EEG signals. ECG electrodes were attached based
on the Einthoven triangle principle that considers the right, left arm, and
pubis (toward left leg) to form an equilateral triangle with the heart in the
center as a moving dipole. EEG Ag/AgCl electrodes were attached to the
participants based on the international 10–20 system and bipolar montage
principles.

Subjective workload.—Both the NASA-TLX and the RSME were used
as subjective workload measures. The NASA-TLX is a subjective mental
workload assessment tool based on a weighted average rating on six sub-
scales: mental demand, physical demand, temporal demand, performance,
effort, and frustration level. Ratings for each of the six subscales, with an-
chors 0: Least taxing and 100: Most taxing, were obtained from the partici-
pants at the end of the task. Weights with anchors 0: Least relevant and 5:
Most relevant were determined by the participants' pairwise comparisons
of the subscales most relevant to mental workload (a total 15 of combina-
torial pairs). The ratings and weights were then combined to calculate an
overall workload score. The NASA-TLX score has been reported as highly
correlated with various objective mental workload measures (Perry, Sheik-
Nainar, Segall, Ma, & Kaber, 2008). The NASA-TLX reliability for repeated-
measures was about .77 (Battiste & Bortolussi, 1988).
The Rating Scale on Mental Effort (RSME) is a unidimensional mea-
sure that assesses mental workload with a vertical line that contains nine
anchor points with descriptive labels ranging from “Absolutely no ef-
fort” (close to 0), through “Rather much effort” (about 57), to “Extreme
effort” (about 112) on the 0–150 scale. A subjective response was recorded
by marking the line at the score corresponding to the mental workload
taken to operate the task. The RSME has been reported to be sensitive to
mental workload in both applied settings (e.g., De Waard, 1996; Lin &
Cai, 2009) and in the laboratory (e.g., Mulder, Dijksterhuis, Stuiver, & de
Waard, 2009). De Waard (1996) found that the RSME was sensitive to the
mental state when the performance measures had not declined, but opera-
tors were trying to maintain their performance by increasing effort.
Experimental Task
The experimental task, the NASA-MATB (National Aeronautics and
Space Administration Multi-Attribute Task Battery; original version), was
used as the multiple task. The task simulates multiple task activities and
has been applied in many mental workload studies. It includes four sub-
tasks: system monitoring, resource management, communication, and track-
ing. For the system-monitoring task, the participants were asked to moni-
tor lights in the upper left corner of a computer screen. The participants
had to press a button when a change in the lights on/off status occurred.
There were also four vertical pointers that oscillated at different frequen-
cies located below the window. The participant had to press the button if
the range of movement for these pointers was too large. For the resource
management task, they had to maintain the volume in two fuel tanks be-
tween 2,000 and 3,000 units by adjusting a pump located at the bottom
center of the window using the keyboard. For the communication task,
they had to determine whether their own codes were called over the ra-

B-W. HSU, ET AL.100
dio and modify their flight parameters as directed using the keyboard. The
task difficulty could be distinguished by the changing time and frequen-
cy of these events. For the tracking task, the participants had to monitor a
block located at the top center of the window by maintaining cursors that
displayed irregular movements within a dotted box using a joystick. This
task can be made automatic (computer controlled) to simulate the autopilot
(Borghini, Aricò, Astolfi, Toppi, Cincotti, Mattia, et al., 2013). In this study,
only the system monitoring, resource management, and communication
tasks were adapted in the formal experiment. In order to closely simulate
the real flight conditions, the tracking task was set to auto to simulate the
autopilot control system environment that is frequently used in aviation.
The MATB performance operation accuracy data were collected us-
ing MATB processing software. The operation accuracy rate was defined
as the proportion of correct responses excluding false alarms and misses
(Fairclough, et al., 2005). A miss was recorded if the participants did not
respond to any target within 15sec.
The ambient experimental environment was controlled at a tempera-
ture of 26˚C with a relative humidity between 60 and 65%. The experimen-
tal set-up is shown in Fig. 1.
Experiment Procedure
The participants were asked to perform the MATB at three difficul-
ties to represent the three different mental workload conditions. It was
hypothesized that the workload would increase with the increase in task
difficulty, and the operating effort would reflect the difficulty in these
FIG. 1. Experimental environment set-up

commonly used physiological indices in the same way as found in the lit-
erature. The difficulty levels were: low (few events; participants may be
bored and their concentration may be low), medium (regular events; most
participants are able to operate at a normal pace and achieve good perfor-
mance), and high (many events; participants must pay considerable atten-
tion to maintain operating performance).
Five participants, none of whom took part in the formal experiment,
were recruited for a pilot study to ensure the task difficulty levels were
adequate. At the end of each task, the NASA-TLX and RSME scores were
collected and the operation accuracy rates were calculated. The average
scores a for the low, medium, and high workload levels in the NASA-TLX
were 15.5, 38.0, and 64.5, respectively; the RSME average scores were 27.5,
45.0, and 72.5, respectively; the accuracy rates were 100, 96.25, and 92.96%,
respectively. The results showed a positive trend in increased subjective
workload with increasing difficulty with the operation accuracy above
90%. This implies that the participants still could reach high performance
under the different workloads at the pre-specified difficulty levels. The
three-task difficulty settings in this study are based on a revised version
of the MATB study (Fournier, et al., 1999), and are listed in Table 1. These
task difficulty levels were applied in the formal task to represent the low,
medium, and high workload.
Substantial training was given to the participants before the formal
experiment to ensure that they could operate the MATB with pre-speci-
fied proficiency and avoid a learning effect. The participants were asked
to keep practicing until their performance became stable (Fairclough, et al.
(2005). More than 80min. was required for each participant to demonstrate
stable performance. The tasks used during practice were designed specifi-
cally to develop skill and were different from the tasks used in the formal
experiment.
The participants were then instructed not to talk and to avoid unnec-
essary movements to eliminate artifacts while collecting their physiological
data. Each one completed a consent form before the formal experiment. A
5-min. break was given before the formal experiment to prevent fatigue. The
TABLE 1
THE NASA-MATB TASK DIFFICULTY SETTINGS
n Times in 10min.
Task Low Level (L) Medium Level (M) High Level (H)
System monitoring 3 28 46
Resource management 3 7 10
Communications 2 5 15

B-W. HSU, ET AL.102
formal experiment involved four stages: one baseline stage and three opera-
tion stages. In the baseline stage, the participants were asked to lie back, sit,
and perform passive watching of the MATB tasks without actively perform-
ing for 10min. while the baseline data were collected. Each task for the three
operation stages lasted 10min. with three difficulty levels randomized. The
participants were not informed of the difficulty level of the ongoing task to
avoid any expectation or habituation effects. The physiological mental work-
load measures were recorded in each of the four stages. At the end of each
operation stage, the participants were asked to complete the NASA-TLX and
the RSME. The participants rested for 5min. before the next stage.
In the operation stage, to minimize the confounding effect of the op-
erating strategy, the participants were told to maintain performance with
maximal accuracy and that the effort should be focused on maximizing ac-
curacy instead of pursuing speed while performing the MATB. To ensure
the participants adhered to this strategy, performance data (operation ac-
curacy rate) were monitored at the end of the operation stage. All of the
data were adopted only if the participants could reach at least 90% opera-
tion accuracy.
Data Collection
As for EEG data recording, bipolar montage was applied in the pres-
ent experiment. Bipolar montage is a basic pattern of connections between
EEG electrodes and recording channels. To reduce movement artifacts in
the operational environment (AlZoubi, Calvo, & Stevens, 2009), EEG elec-
trodes were attached to the right frontal-central (F4-C4) areas and left-
right occipital (O1-O2) areas of the subject's cortex. Mikhail and El-Ayat
(2013) suggested that the use of four or six EEG electrodes might reach ac-
ceptable accuracy in mental state measurement.
EEG recording sites were chosen based on the literature. The reasons
for choosing the F4-C4 sites were that the frontal lobes are associated with
impulse control, decision judgment, motor function, and problem solv-
ing. Mental and cognitive processing usually occurs in the frontal and cen-
tral lobes. Previous studies indicated that EEG θ increases with increasing
task difficulty and was found to be most profound over frontal site (Jen-
sen & Tesche, 2002; Esposito, Aragri, Piccoli, Tedeschi, Goebel, & Di Salle,
2009) and right hemisphere (Rietschel, Miller, Gentili, Goodman, McDon-
ald, & Hatfield, 2012). The reasons for choosing O1-O2 regions are due to
the finding of Dussault, Jouanin, Philippe, and Guezennec (2005) that in-
creased workload in flight simulation resulted in greater θ activity in the
occipital lobe. Furthermore, Hong, et al. (2012) indicated that the occipital
site tends to be more active as task difficulty increases. Thus, the present
study chose the left-right occipital (O1-O2) and right frontal-central (F4-
C4) areas as the region for collecting EEG signals.

The EEG signals were acquired using a Nexus-10 DC-coupled EEG por-
table amplifier incorporating a 24-bit A/D converter with BioTrace+soft-
ware. They were sampled at 1024Hz using two bipolar channels, refer-
enced to the left mastoid ground electrode (impedance<5k Ω), which was
jointly used with the ECG measures. Raw data were processed using Bio-
Trace+software and with an IIR band pass filter (3rd order) to remove
interference from ocular, head, and muscle movements. Prior to signal
processing, a 60Hz notch filter was applied to remove environmental ar-
tifacts. Fewer than 5% of all epochs were excluded from the time series.
Spikes and excursions were identified when the EEG amplitude changed
significantly (e.g.,>40μV) over a short duration (e.g., 12–27msec.). Am-
plifier saturation was recognized when the change in amplitude between
two data points exceeded the predefined threshold (e.g., 440μV) or the
EEG amplitude approached the max/min of the amplifier dynamic range.
Fast Fourier Transform (FFT) was conducted to extract α, β, and θ power
of brainwaves. To normalize the distribution of the data, all EEG power
values were transformed using a natural logarithm. These data were then
transformed back into time-domain indices and calculated using the root
mean square (RMS) into 32 outputs per second. The mean and ratio ampli-
tude of each rhythm, i.e., α/θ and θ/β, were further calculated to evaluate
the mental workload. A total of 10 EEG indices were obtained (α, β, θ, α/θ,
and θ/β from O1-O2 and F4-C4, respectively).
Thirteen indices were obtained for ECG data collection, including
HR and 12 HRV indices. The HRV were calculated in accordance with
the HRV Measurement Standards (Malik, 1996). The inter-beat intervals
(IBIs) were processed first for each 5-min. interval using BioTrace+soft-
ware. Five-min. segments between the first and ninth min. of each stage
were decided to avoid start- and end-related effects. The relatively stable
waveforms were selected for calculation. The software eliminates the un-
likely IBI values (i.e., below 40 beats per minute and over 240 beats per
minute) and eliminates the peaks that contain too much noise or have a
difference exceeding 30 BPM (beat per minutes) compared to the last de-
tected beat. The data for IBIs were then transformed into equidistant time
series using cubic spline interpolation and resampled at 512Hz. The data
were smoothed using the Hamming window once the interpolation was
completed. The data were then transformed into power spectra with FFT
for calculating the 6 frequency-domain HRV. Overall, 12 HRV indices, six
time domain HRV indices, and six frequency domain HRV indices were
obtained in the present study (see Table 2).
Statistical Analysis
It was necessary to use baseline data to make comparisons, to elimi-
nate the individual differences effect on physiological responses. All phys-

B-W. HSU, ET AL.104
iological measures were normalized per participant by calculating the ra-
tio of the average processed recording data and the baseline data (Ros,
Munneke, Ruge, Gruzelier, & Rothwell, 2010). A one-way analysis of vari-
ance (ANOVA) with repeated-measures (n=30) was employed to deter-
mine whether the change in mental workload would cause a significant
change in these response measures. The Greenhouse and Geisser epsilon
correlation was applied for the degrees-of-freedom adjustment. A Pearson
correlation analysis was conducted to identify the zero-order relationship
between physiological indices and subjective assessments in this study.
A 5% significance level was adopted in all tests. The effect size data (eta
square, η2
) were also provided.
RESULTS
The results of the performance data showed that all the participants
consistently reached at least a 90% accuracy rate through all three opera-
tion stages. These results implied that the participants stably maintained
a high performance in this experiment, corresponding with the research
goal. The descriptive statistics, the one-way ANOVA with repeated-mea-
sures, and the changing trend (line charts) of these 25 indices (10 EEG in-
dices, 13 ECG indices, and 2 subjective measures) are presented in Table 3
and Fig. 2. Box plots of the indices that showed significant effects on men-
tal workload are presented in Fig. 3. Table 4 displays the Duncan post hoc
test results indicating the mental workload measurement index sensitivity.
TABLE 2
TIME DOMAIN AND FREQUENCY DOMAIN MEASURES OF HRV
Index Description
Time domain HRV
NNMin Smallest IBI found (NN=IBI)
NNMax Largest IBI found
NNMean Mean IBI found
SDNN Standard deviation of all IBI of the data set
RMSSD The square root of the mean of the sum of the squares of differ-
ence between successive IBIs differences
pNN50 Percentage of NN50 intervals
Frequency domain HRV
VLF Very low frequency component 0–0.04Hz
LF Low frequency component 0.04–0.15Hz
HF High frequency component 0.15–0.4Hz
LF/HF Ratio of the LF and HF components
%LF Percentage of LF in the entire spectrum
%HF Percentage of HF in the entire spectrum

TABLE 3
NORMALIZED DESCRIPTIVE STATISTICS FOR EEG, ECG, AND SUBJECTIVE MEASURE, AND
ANOVA RESULTS (N=30)
Dependent
Variable
Low
(L)
Medium
(M)
High
(H)
ANOVA Result
η2
M SD M SD M SD df
df
error
F p
EEG
F4-C4
α 0.84 0.18 0.82 0.19 0.83 0.26 1.41 40.77 0.47 .56 0.02
β 0.99 0.30 0.99 0.33 1.04 0.45 1.45 42.16 1.65 .21 0.05
θ 1.04 0.23 1.07 0.25 1.11 0.32 1.59 45.95 6.10 .01† 0.17
α/θ 0.84 0.17 0.80 0.16 0.77 0.15 2 58 18.17 .001‡ 0.39
θ/β 1.06 0.18 1.11 0.21 1.11 0.23 1.61 46.59 3.04 .07 0.10
O1-O2
α 0.95 0.32 0.92 0.28 0.94 0.33 1.59 45.96 0.65 .49 0.02
β 1.14 0.43 1.10 0.29 1.15 0.36 1.46 42.23 0.73 .45 0.03
θ 1.15 0.28 1.14 0.23 1.18 0.26 1.34 38.90 1.23 .29 0.04
α/θ 0.83 0.14 0.80 0.15 0.78 0.15 1.51 43.69 1.11 .06 0.10
θ/β 1.03 0.18 1.05 0.17 1.04 0.18 1 29.01 0.98 .33 0.03
ECG
Heart rate, bpm 1.01 0.06 1.03 0.14 1.03 0.10 1.52 44.87 1.06 .34 0.04
Time domain HRV
NNMin 1.05 0.18 1.06 0.17 1.08 0.17 2 58 1.64 .20 0.05
NNMax 1.01 0.11 1.00 0.09 0.98 0.09 2 58 1.72 .19 0.06
NNMean 1.02 0.93 1.04 0.14 1.04 0.15 2 58 1.11 .34 0.04
SDNN 0.97 0.31 0.89 0.18 0.78 0.20 1.59 45.96 4.01 .002† 0.22
RMSSD 1.11 0.33 1.01 0.23 1.00 0.28 2 58 1.33 .06 0.10
pNN50 1.23 0.63 1.13 0.59 0.97 0.54 1.49 43.18 0.34 .65 0.01
Frequency domain HRV
VLF 0.89 0.87 0.81 0.94 0.38 0.41 2 58 6.88 .004† 0.17
LF 1.16 0.73 0.80 0.52 0.53 0.54 1.53 43.78 4.72 .02* 0.15
HF 1.08 0.66 0.92 0.52 0.83 0.64 1.34 30.16 1.25 .21 0.05
LF/HF 1.25 1.11 0.96 0.78 0.74 0.73 1.37 39.63 26.85 .001‡ 0.48
%LF 1.30 0.52 1.10 0.43 1.08 0.43 2 58 6.80 .002† 0.19
%HF 1.43 1.14 1.60 1.12 2.09 1.26 2 42 20.66 .001‡ 0.46
Subjective assessment
NASA-TLX 21.63 10.84 39.38 9.06 57.31 11.92 2 58 81.60 .001‡ 0.84
RSME 29.07 12.98 54.30 15.36 76.00 13.99 2 58 64.71 .001‡ 0.87
Note.—Low, Medium, High are workload levels. η2
is the eﬀect size. *p<.05. †p<.01. ‡p<.001.

B-W. HSU, ET AL.106
EEG Indices in Performing Multitasking Workload
From Figs. 2A and 2B, the three EEG indices, θ from F4-C4 and α/θ
from both O1-O2 and F4-C4, showed increasing or decreasing trend as the
mental workload increased. Furthermore, the ANOVA results in Table 3
NormalizedMeanValue
NormalizedMeanValue
NormalizedMeanValue
HR and Time Domain HRV Trend
NormalizedMeanValue
MeanValue
Frequency Domain HRV Trend Subjective Measure Trend
FIG. 2. Trends of the 25 indices on diﬀerent mental workload levels. (A) the trends of
EEG (O1-O2) indices; (B) the trends of EEG (F4-C4) indices; (C) the trends of HR and time-
domain HRV indices; (D) the trends of frequency-domain HRV indices; (E) the trends of the
RSME and the NASA-TLX; L: low mental workload level; M: medium mental workload
level; H: high mental workload level.

FIG. 3. Box-plots of the indices that showed significant effects for the different mental
workloads. In each box, the middle mark is the median; the edges of the box are the 25th and
the 75th percentiles. (A) the EEG θ (F4-C4); (B) the EEG α/θ (F4-C4); (C) the SDNN index;
(D) the VLF index; (E) the LF index; (F) the LF/HF index; (G) the %LF index; (H) the %HF
index; (I) the NASA-TLX measure; (J) the RSME measure; L: low mental workload level; M:
medium mental workload level; H: high mental workload level.

B-W. HSU, ET AL.108
TABLE 4
THE DUNCAN POST HOC TEST RESULTS FOR THE MENTAL
WORKLOAD INDICES
Index Low Medium High
(F4-C4) EEG θ 1.04 1.07 1.11
A
B B
(F4-C4) EEG α/θ 0.84 0.80 0.77
A A
B B
SDNN 0.97 0.89 0.78
A A
B
VLF 0.89 0.81 0.38
A A
B
LF 1.16 0.80 0.53
A A
B B
LF/HF 1.25 0.96 0.74
A A
B B
%LF 1.30 1.10 1.08
A
B B
%HF 1.43 1.60 2.09
A A
B
NASA-TLX 21.63 39.38 57.31
A
B
C
RSME 29.07 54.3 76
A
B
C
Note.—The letters A, B, and C indicate significant differenc-
es among the mental workload levels at the Duncan post hoc
testing (p<.05). The Duncan grouping with the same alpha-
betical letter indicates that no significant differences exist
between the measures. Digital numerals represent the mean
normalized units value of each index.

show the EEG θ (F1.59,45.95
=6.10, p<.01, η2
=0.17) and α/θ index (F2,58
=18.17,
p<.001, η2
=0.39; F4-C4) were the only two EEG indices that showed sig-
nificant effects for the different mental workloads. It is worth noting that
in Table 3 the EEG α/θ from F4-C4 showed significant results, but the EEG
α (F4-C4) itself did not show any significant variations. It is perhaps due to
the significant changes in EEG θ and not to the ratio between EEG α and
θ. However, the ratio calculation still amplifies the effect size of the index.
Additionally, in Table 3 the EEG indices from O1-O2 did not show any sig-
nificant effect related to mental workload. This implies that the EEG signal
obtained from F4-C4 was more sensitive than that obtained from O1-O2 in
reflecting mental workload.
Furthermore, Table 4 displays the EEG α/θ (F4-C4) could clearly dif-
ferentiate high and low workloads. EEG θ (F4-C4) could differentiate be-
tween medium and low workloads. The results indicate that EEG α/θ and
EEG θ (F4-C4) showed different sensitivity to differences in mental work-
load. Moreover, it appears that EEG α/θ (F4-C4) is more practical than
EEG θ (F4-C4) in measuring mental workload.
ECG Indices in Performing Multitasking Workload
Table 3 and Figs. 2C and 2D show that 3 time-domain HRV indices:
NNMin, NNMax, and SDNN and 5 frequency-domain HRV indices: VLF,
LF, LF/HF, %LF, and %HF showed relatively stable increasing or decreas-
ing trends as the mental workload increased. HR did not show significant
effects under different mental workloads. Table 3 shows that almost all of
the frequency domain ECG indices (5 of 6) and only one time domain HRV
index (SDNN) showed significant effects. It seems that the frequency do-
main HRV is more sensitive than the time domain HRV in reflecting the
mental workloads produced in this study.
Table 4 shows the LF/HF and LF could effectively differentiate high
and low mental workloads. The SDNN, VLF, and %HF could effectively
differentiate medium and high mental workloads. However, the %LF could
only differentiate medium and low mental workloads. These results imply
that each HRV index has different sensitivity to changes in mental work-
load. Overall, the SDNN, VLF, LF, LF/HF, and %HF seem to be sensitive
enough to differentiate the high mental workloads as specified in this study.
Subjective Measures of Workload in Performing Multitasking
From Fig. 2 (E) and Figs. 3 (I), (J), the two subjective measures, the
NASA-TLX and the RSME, showed stable increasing trends with increas-
es in mental workload and the relatively small differences in standard
deviations among each stage. The NASA-TLX mean scores for the three
workloads were about 22, 40, and 57 points, respectively. The RSME mean
scores were about 29, 54, and 76, respectively. The ANOVA results show

B-W. HSU, ET AL.110
that different applied mental workloads caused significant changes with
a large effect size in both subjective assessments (NASA-TLX: F2,58
=81.60,
p<.001, η2
=0.84; RSME: F2,58
=64.71, p<.001, η2
=0.87). This implies that
the participants were able to discriminate between task difficulty levels.
Moreover, the results in Table 4 confirm that the two assessments could ef-
fectively differentiate low, medium, and high mental workloads, and were
highly sensitive in measuring mental workload produced in this study.
Correlation Analysis
A Pearson correlation analysis was conducted to identify the zero-or-
der relationship between these indices that showed significant effects in
mental workload evaluations in this study. The correlation results are
presented in Table 5. Since there are 45 correlations, it is likely that with
α=.05 a minimum 2 to 3 of these correlations belong to type one errors. To
avoid the analysis bias, the significance level was specified at p<.01. Ta-
ble 5 showed that some physiological indices of the mental workload cor-
related highly with each other; i.e., LF/HF correlated significantly with
the other frequency-domain HRV indices as well as the EEG α/θ and
EEG θ indices. This indicates that measuring LF/HF reflects the signifi-
cant changes shown by other physiological indices across different mental
workloads while performing multiple tasks. Some physiological indices,
e.g., %LF, did not have significant correlations with other physiological in-
dices, and the EEG θ (F4-C4), SDNN, VLF, LF, %LF, and %HF did not have
significant correlations with either subjective measure.
TABLE 5
PEARSON CORRELATION ANALYSIS RESULTS (CORRELATION COEFFICIENT: R)
EEG θ
(F4-C4)
EEG α/θ
(F4-C4)
ECG NASA-
TLX
RSME
SDNN VLF LF LF/HF %LF %HF
EEG θ
(F4-C4) −.43* .11 .04 .16 .35* .13 −.38* .10 .25
EEG α/θ
(F4-C4) .11 .11 −.11 −.33* −.20 −.41* −.45* −.41*
SDNN .20 .15 .19 −.06 −.13 −.12 −.19
VLF .42* .37* −.14 −.31* −.18 −.22
LF .31* .33* −.13 −.13 −.10
LF/HF .62* −.48* −.25 −.32*
%LF .12 −.16 −.10
%HF .20 −.19
NASA-TLX .89*
RSME
*p<.01.

As to the correlation between subjective assessments and physiolog-
ical indices, the RSME correlated significantly with the two physiologi-
cal indices: EEG α/θ (F4-C4) and LF/HF; the NASA-TLX correlated sig-
nificantly with only one physiological index: EEG α/θ (F4-C4). EEG α/θ
(F4-C4) showed significant correlations with the two subjective assess-
ments. It is worth noting that the time domain HRV index, SDNN, did not
show any significant correlation with all other physiological and subjec-
tive measures.
In summary, these results indicate that the EEG α/θ (F4-C4) showed
stronger correlations with subjective measures than the ECG indices.Among
all of the physiological indices, LF/HF showed the highest correlations
with other physiological indices.
DISCUSSION
This research aims to make contributions to identify physiological in-
dices that accurately monitor mental workload while participants perform
multitasks (MATB) with the stable performance and minimal human error
strategy. Compared with previous works using the MATB to investigate
mental workload, the main novelty is that the operation strategy with sta-
ble performance and minimal human error were specified in this study.
Among the EEG-based measures, the results showed that EEG θ and
EEG α/θ from the frontal-central area were effective for measuring men-
tal workload. Previous studies indicated that the EEG θ power was aug-
mented and the α activity was suppressed from baseline when perform-
ing a MATB task (Fairclough & Venables, 2006). In the present study, the
θ power showed similar trends with the above literature, except for the α
power. The inconsistencies might be due to different operation strategies.
The participants in the present study focused on stable performance and
accuracy instead of speed. That means they maintained stable conscious-
ness and alert state under this situation, which might keep α activity not
significantly suppressed. Additionally, the results indicate that the EEG
α/θ (F4-C4) was sensitive to differentiating high and low mental work-
loads and significantly correlated with the subjective assessments of men-
tal workload. This was consistent with the finding from previous studies
that the EEG α/θ is sensitive to measuring the mental and alertness state
of operators (Cote, et al., 2003). However, it is important to note that the
correlation coefficients for EEG α/θ with the NASA-TLX and the RSME
were both about r=.4. This means they only share about 16% of their vari-
ance. Thus, although the EEG α/θ might be considered a valid and ef-
fective indicator for monitoring mental workload in performing multiple
tasks, it was reasonable to recommend that it can be used together with
other valid mental workload indices to augment the validity.

B-W. HSU, ET AL.112
As to the EEG θ/β, Putman, et al. (2010) indicated that frontal EEG
θ/β was inversely correlated with inhibitory function. In this study, the
EEG θ/β (F4-C4) did not show significant effects on mental workload.
However, it presented differences in low-medium but almost no differ-
ence between medium-high workloads. The phenomenon might be due
to different operation strategies. In the medium-high workload condition,
the operation strategy might facilitate inhibition to decrease the rise of
index value. It was reasonable to infer that the maintained stable perfor-
mance and accuracy strategy would affect the sensitivity and validity of
the EEG θ/β (F4-C4) index.
As for the location of the EEG recording, the present study showed
that the bipolar montage EEG recordings from the right frontal–central
sites are effective in mental workload measurement. This is consistent
with the findings of Meng, Hu, Wang, and Qu (2006) that the right fron-
tal and right central electrode sites take on perceptual and cognitive loads
that are responsible for the functions relevant to the required mental func-
tions during multiple tasks. However, the present study did not show sig-
nificant effects on the bipolar montage EEG data recorded from occipital
sites. This also implies that no significant asymmetric effect between the
left and right occipital lobes was found even though the multiple tasks in-
volved visual perception and information cognition. Compared with the
previous research (Dussault, et al., 2005), the non-significant effects on oc-
cipital lobe might due to the short task time (1hr.), which produced a vi-
sual load not long enough to result in significant hemisphere effects on the
human visual center.
Regarding the ECG measures, the results of this study revealed that
HR did not show any significant effect by mental workload levels. This
is consistent with the findings of Miyake, et al. (2009) in a MATB related
study. They also concluded that the direction of HR change induced by
mental tasks depends on the task characteristics. The task characteristics
might increase or decrease HR or evoke no HR change. This also indicates
the limitation of using HR in mental workload measurement while per-
forming multiple tasks.
The present study showed that frequency domain HRV indices are
useful tools for measuring mental workload in multiple tasks. Among the
frequency domain HRV indices, LF and LF/HF were the most effective
measures with significant decreasing trends while the mental workload
increased. Furthermore, HF did not show any significant effects. Taelman,
Vandeput, Gligorijević, Spaepen, and Van Huffel (2011) indicated that the
change in HF was related to the respiration frequency, as the main peak in
the HF is normally linked with respiration. In this study the participants
were focused on stable performance, maximizing accuracy while per-

forming the task. This strategy might change the mental state and main-
tain stable respiration frequency and HF tendency in the participants. A
similar phenomenon was also found in EEG α power that presented non-
significant variation in this study. Additionally, LF/HF and LF decreased
with increasing workload. This is perhaps due to the sympathovagal bal-
ance switch toward a dominance of parasympathetic activity (Cinaz, et al.,
2010). Therefore, to understand the operation strategy is an important is-
sue in measuring mental workload while performing multiple tasks.
The present results revealed that while almost all of the time-domain
HRV indices (except SDNN) did not show significant change as the men-
tal workload increased, some indices such as NNMin still showed a clear
trend with the increase in mental workload. These results are similar to
those from a previous study by Mukherjee, Yadiv, Yung, Zajdel, and Oken
(2011) with short-term mental workload recording. Conversely, Malik, et
al. (1996) reported that the time-domain HRV had high representativeness
in long period recording (24hr.). Thus, the lack of statistically significant
changes in these time domain HRV indices may be because the task time
was not long enough. This phenomenon was consistent with the findings
of Maestri, Raczak, Danilowicz-Szymanowicz, Torunski, Sukiennik, Ku-
bica, et al. (2010).
In summary, the LF/HF showed significant correlations with all oth-
er frequency-domain HRV indices and both the EEG indices and one sub-
jective measurer. This implies that measuring LF/HF reflects the signifi-
cant changes shown by other physiological indices or subjective measures
across different mental workloads while performing multiple tasks. Addi-
tionally, the Duncan post hoc test results showed that the LF/HF could ef-
fectively differentiate high from low workload, indicating that LF/HF is
sensitive and can be used as an indicator for high mental workload. This
finding is consistent with the report from previous studies that LF/HF is a
good index of cardiac sympathetic nerve activity aroused by high mental
workload (Mukherjee, et al., 2011).
The effect sizes of all the physiological measures applied in this study
were not large. Thus, how to improve the effect size of these physiological
indices requires further investigation.
Limitations and Conclusion
Some limitations of this study should be mentioned. First, this study
recruited 30 participants, the lower bound of the central limit theorem.
It would increase the confidence of the Duncan post hoc tests and corre-
lation analysis results if the number of participants were increased. Sec-
ond, to ensure that participants adhered to the strategy of maintaining
stable operations with maximal accuracy in the formal experiment, this
study only checked if the operation accuracy rate reached at least 90% at

B-W. HSU, ET AL.114
the end of each operation stage. The inclusion of additional behavioral re-
sults or performance measures (e.g., speed, RMS error, reaction time) to
double check the actual adoption of the specified operation strategy might
increase the assurance of experiment execution quality. Third, this study
only used few EEG electrodes. The limited number of EEG electrodes
might provide less accurate and detailed determination of the brain's elec-
trical activity. It would be interesting to investigate the entire brain and
not just on the right frontal-central and occipital areas. For further study,
it is perhaps more suitable to use more cerebral electrodes to verify if such
a low number of derivations could be still adequate to reveal and repre-
sent the phenomena. Some different results may be obtained if more EEG
data were collected from some other regions, such as the parietal lobe or
temporal lobe. Fourth, the participants' physiological responses were col-
lected within 10min. intervals. It would be interesting to perform time-
frequency analysis on the response measures using shorter time Fourier
transform or wavelet transform techniques. The above-mentioned limita-
tions should be addressed in future investigations.
The EEG α/θ (frontal-central lobe) and LF/HF were effective in re-
flecting differences in mental workload while the participants performed
multiple tasks with the strategy to maintain stable performance and max-
imize accuracy, especially under high mental workload conditions. In a
multiple task situation with limited time and mental resources such as
process monitoring and piloting, the LF/HF reflects the significant chang-
es shown by other physiological indices or subjective measure across dif-
ferent mental workloads. The EEG α/θ (frontal-central lobe) reflects the
significant changes shown by both of the subjective measures. These re-
sults bring relevant information for research on the mental workload
monitoring of human operators in multitasking situations. The findings
can also be applied to other mission critical scenarios.
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Accepted June 16, 2015.

EFFECTIVE INDICES FOR MONITORING MENTAL WORKLOAD

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