IEEE Systems, Man and Cybernetics Magazine - January 2022 - 8

message with the call sign " NASA504 " must be executed
correctly to determine whether message with the call sign
" UPS207 " must be overlooked. Hence, the quantity of the
audio messages with different call signs controls the task
load of COMM events.
Subjective Workload
It is important to have an idea about how each participant
thinks of the task load in each condition. Hence,
subjective workload rating was calculated by utilizing a
NASA-TLX workload questionnaire [46]. The NASA-TLX
workload questionnaire pops up on the monitor at the
end of the actual MATB-II trials. There are six factors,
consisting of mental demand, physical demand, temporal
demand, performance, effort, and frustration, measured
by the NASA-TLX. The participant is instructed
to give a rating on a scale for each factor considering
the contribution of the factor to the task load according
to the participant's perception. After the rating of all of
the factors is complete, an overall task load can be calculated.
The score ranges between 0 (minimum) and
100 (maximum). Here, the lower the score, the lesser
the perceived workload.
Baseline Method
EEG data are generally believed to be very person-specific
in nature [42]. The slightest variation of the brain activity
of the participant during the test can have a significant
effect on the EEG signals and, definitely, such variation is
beyond the control of the participant. EEG signals vary
across different participants and also across the different
session for the same subject [32]. Hence, the performance
of the proposed method is necessarily required to be verified
against a model trained with individual data. In this
type of model, training data (used to train the model) as
well as the test data (used to evaluate the performance of
the model) are collected from the very same subject. Here,
we refer to such a model as the baseline.
In our study, 80% of the available data per participant is
used for training the model and the performance of the
model is evaluated from the remaining 20% data in a
10-fold repeated cross-validation setup with 10 repeats. To
ensure a fair comparison, the structure of the model used
in the baseline (i.e., user-specific) method is also used in
the TL setup (proposed method). For example, user-specific
training data are used to train an RF model in the
baseline methods. The same RF with the very same
Figure 1. The MATB-II interface [39].
8
IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE January 2022

IEEE Systems, Man and Cybernetics Magazine - January 2022

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