IEEE Systems, Man and Cybernetics Magazine - October 2021 - 26

functions and processes. Irrespective of whether the elicited
spiking pattern of neurons is continuously sustained
or discretely dynamic, it is believed that high-order cortical
areas, such as the PFC, are highly involved [23], [24].
Therefore, it is interesting to check where the network
focus (the ROI) is capable of distinguishing among the
workload extent or not during different workloads exerted
on the working memory.
Experiment and Data
Figure 1 shows the overall setup of the experiment, which
is used to investigate the behavior of a given network
employed to analyze the EEG data captured during a
working memory capacity test [25]. The paradigm of the
working memory task for EEG data set acquisition is as
follows. A randomized letter set is displayed for the participant
at 0 s relative to the beginning of the current trial. It
lasts for 0.5 s then fades away. At 3.5 s, a letter appears on
the screen, and the subject must decide whether the letter
belongs to the previously shown letter set or not by perceiving
the letter. After the subject's action, a fixation icon
launches a new trial.
The EEG signals are consecutively captured during the
(a)
β
α
θ
Channels
(b)
θ
(c)
Figure 2. The preparations of topographical EEG
data in the spatial domain from converting waveform
EEG data in the time domain. (a) Raw waveform EEG
data in the time domain. (b) Frequency components
across channels via Fourier transform onto segmental
data. (c) Channel-location-preserving EEG topographies
in the spatial domain by inter- and extrapolation of
EEG subband values.
α
β
entire session, which comprises various trials, and the data
preparation is the same as in [2]. In detail, the EEG time
series data lasting for 3.5 s are sliced into seven nonoverlapping
segments. Then, a Fourier transform is applied to each
segment to obtain the power spectrum, up to 30 Hz for each
channel. According to the literature, which reveals the
effectiveness of using respective EEG subbands [25], [26],
three bands, i.e., theta (4-7), alpha (8-13), and beta (14-30 Hz)
are considered in this research. For each band of the individual
channel, the squared absolute values within the frequency
band are added up to measure the contribution of
the electrode source. Finally, together with the corresponding
EEG montage used in the experiment, the topographical
representations are generated. Figure 2 shows the
overall procedures in an illustrative manner. Figure 2(a)
displays the EEG waveform data in the time domain, and
Figure 2(b) presents each segment converted into the frequency
domain by a fast Fourier transform. The x- and
y-axes represent the channels and absolute values, respectively,
of the frequency components, while Figure 2(c)
depicts the EEG topography generated from the frequency
data by interpolation and extrapolation, according to the
coordinates of the placement.
Network Architecture
The corresponding constructed DNN for analyzing EEG
data to unveil the network dynamics is illustrated in
Shared
Feature Maps of
the Last Step
Recur-Convnet
GAP
Convnet (Conv + Pool)
Figure 3. The architecture of the neural network for analyzing EEG data. FC: fully connected; recur: recurrent;
convnet: convolutional network.
26 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE October 2021
FC
Frequencies

IEEE Systems, Man and Cybernetics Magazine - October 2021

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