IEEE Systems, Man and Cybernetics Magazine - January 2022 - 6
programming [27] and extreme
learning machine [28], and so on. The
extensive use of RF-based classifiers
for automated sleep stage identification
[9]; early seizure detection
[8], [10]; mental fatigue detection
[29]; and Alzheimer's disease recognition
[11] is also noticeable.
However, for cognitive load level
detection, researchers have not
used RF classifiers very extensively.
Among the few published reports
that we have been able to trace
back, a 12: 59% error on test set, or 87: 41% accuracy has
been achieved by researchers in [30] using RFs for working
memory load. In another work, adapted form of Sternberg
memory task [31] has been used and a deep learning-based
method has been compared against an RF classifier. 92.36%
accuracy has been achieved by using a fused CNN. In most
works, primary focus on cognitive load identification circles
around deep learning-based methods [32]-[34].
In our study, Multi-Attribute Task Battery (MATB)-II [35],
We take advantage
of brain signals,
one of the most
efficient indicators of
cognitive functions,
in this work.
took place among potential volunteers
to screen for respiratory,
neurological, and cardiovascular
disorders.
A formal ethics approval was
obtained from the Human Ethics
Advisory Group of the Faculty of
Science, Engineering and Built
Environment, Deakin University,
Australia. The participants provided
full written consent before the
start of the experiment.
which is a popular software and framework for assessing
cognitive workload, is utilized. It can change the workload
by systematically increasing user engagement requirement.
There have been significant works for cognitive load measurement
based on MATB-II [35] framework including load
monitoring [36], NN-based categorization of cognitive load
level (achieved 90% accuracy) [37], cognitive performance
measurement (achieved 80% accuracy achieved on validation
set), and so on. To the best of our knowledge, using an
RF model on a MATB-II data set, no one has been able to
achieve more than 95% accuracy. In this work, we have demonstrated
that 99% accuracy can be achieved in identifying
low, medium, and high cognitive load by a well-learned RF
classifier, while all of the results for seven different participants
were more than 95%. Using the visual assessment of
tendency algorithm for analyzing the basic characteristics
of a data set before forming statistical models is a great
idea. We have achieved a precise concept about these algorithms
from the survey of Kumar and Bezdek [38].
The major contribution of this article lies in the introduction
of a transfer learning (TL) model having the ability to predict
the cognitive load of an unknown user without any
necessary calibration. Upon comparison with some popular
user-specific models, our model demonstrated better performance
than others. With the help of the data from multiple
participants in our experiment, it turns out that our observation
is true for all participants.
Experimental Setup
Participants
There were seven male participants with a mean age of 28.6
± 3.2 years. All of the participants were right-handed, and
they volunteered to take part in the study. A prescreening
6
IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE January 2022
Experimental Setup
The experiment involved collecting EEG data as well as
different physiological data such as heart rate, respiratory
rate, and eye tracking even though only EEG data are used
throughout this study. A 14-channel wireless headset
(Emotiv) was used and the electrodes were placed on the
scalp of the participants in 14 different locations based on
the standard 10-20 electrode system. More specifically,
electrodes are placed on AF3, F7, F3, FC5, T7, P7, O1, O2,
P8, T8, FC6, F4, F8, and AF4. The position at the back
served as a reference for electrode system. All of the electrodes
were hydrated before placement on the scalp to
increase signal conductivity.
In addition to the Emotive EEG headset, all participants
wore a physiological data logger (Equivital EQ02+
LifeMonitor) and a GP3 Gazepoint tracker was placed on
the table in front of the seat to capture eye movements during
the experiment.
At the beginning of the experiment, participants were
asked to go inside the lab and were presented with a plan
language statement (PLS) and consent form, which
detailed the study in which they are taking part. If the participants
agreed after reading through the PLS, they were
asked to sign the consent form to record their willingness
to participate in the study. An instruction manual was provided
to the participants at this stage. The manual contained
detailed instruction on completion of each of the
tasks in the study. Afterward, each of the applicants were
given 5 min to get familiar with the equipment and practice
the tasks. Additional practice time was given for those
who requested it.
After the practice phase was over, each participant was
requested to enter a private room equipped with all of the
necessary instrumental setup to conduct the experiment
smoothly. In the room, physiological tools were mounted
on each participant first. This included a Equivital LifeMonitor
vest and an EEG headwear. The critical part here was
to ensure the best fitting for each participant and calibration
to achieve better connectivity of the devices, which in
turn ensured better quality data. After some careful observation
to eliminate the possibility of any malfunctioning of
the wearables, they were instructed to sit in front of a table
with a computer monitor and Gazepoint GP3 eye-trackers
IEEE Systems, Man and Cybernetics Magazine - January 2022
Table of Contents for the Digital Edition of IEEE Systems, Man and Cybernetics Magazine - January 2022
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IEEE Systems, Man and Cybernetics Magazine - January 2022 - Cover3
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