sensors
ArticleNeural Dynamics of Target Detection via Wireless EEG in
Embodied Cognition
Congying He 1,2,† , Rupesh Kumar Chikara 3,4,† , Chia-Lung Yeh 5 and Li-Wei Ko 1,2,6,7, *
1

2

3

4
5

6

7

*
†



Citation: He, C.; Chikara, R.K.; Yeh,
C.-L.; Ko, L.-W. Neural Dynamics ofTarget Detection via Wireless EEG in
Embodied Cognition. Sensors 2021, 21,5213. https://doi.org/10.3390/
s21155213
Academic Editor: Giovanni Sparacino
Received: 30 June 2021
Accepted: 28 July 2021
Published: 31 July 2021

Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims inpublished maps and institutional affiliations.

Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article

Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University,
Hsinchu 30010, Taiwan; cyst0929.bt07@nycu.edu.tw
Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), National Yang Ming Chiao Tung
University, Hsinchu 30010, Taiwan
Jane and John Justin Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX 76104, USA;rupesh.kumarchikara@uta.edu
Department of Bioengineering, University of Texas at Arlington, Arlington, TX 76109, USA
Information and Communications Research Division, National Chung-Shan Institute of Science andTechnology, Taoyuan 325, Taiwan; austinyeh@msn.com
Department of Electrical and Computer Engineering, and Brain Research Center, National Yang Ming Chiao
Tung University, Hsinchu 30010, Taiwan
Drug Development and Value Creation Research Center, Kaohsiung Medical University,
Kaohsiung 807, Taiwan
Correspondence: lwko@nctu.edu.tw
These authors contributed equally to this work.

Abstract: Embodied cognitive attention detection is important for many real-world applications,such as monitoring attention in daily driving and studying. Exploring how the brain and behavior
are influenced by visual sensory inputs becomes a major challenge in the real world. The neuralactivity of embodied mind cognitive states can be understood through simple symbol experimental
design. However, searching for a particular target in the real world is more complicated than during
a simple symbol experiment in the laboratory setting. Hence, the development of realistic situations
for investigating the neural dynamics of subjects during real-world environments is critical. This
study designed a novel military-inspired target detection task for investigating the neural activities of
performing embodied cognition tasks in the real-world setting. We adopted independent component
analysis (ICA) and electroencephalogram (EEG) dipole source localization methods to study theparticipant’s event-related potentials (ERPs), event-related spectral perturbation (ERSP), and power
spectral density (PSD) during the target detection task using a wireless EEG system, which is moreconvenient for real-life use. Behavioral results showed that the response time in the congruent
condition (582 ms) was shorter than those in the incongruent (666 ms) and nontarget (863 ms)
conditions. Regarding the EEG observation, we observed N200-P300 wave activation in the middle
occipital lobe and P300-N500 wave activation in the right frontal lobe and left motor cortex, which areassociated with attention ERPs. Furthermore, delta (1–4 Hz) and theta (4–7 Hz) band powers in theright frontal lobe, as well as alpha (8–12 Hz) and beta (13–30 Hz) band powers in the left motor cortex
were suppressed, whereas the theta (4–7 Hz) band powers in the middle occipital lobe were increased
considerably in the attention task. Experimental results showed that the embodied body function
influences human mental states and psychological performance under cognition attention tasks.
These neural markers will be also feasible to implement in the real-time brain computer interface.
Novel findings in this study can be helpful for humans to further understand the interaction between
the brain and behavior in multiple target detection conditions in real life.
Keywords: wireless EEG; brain computer interface; embodied cognition; target detection; attention

distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Sensors 2021, 21, 5213. https://doi.org/10.3390/s21155213

https://www.mdpi.com/journal/sensors

Sensors 2021, 21, 5213

2 of 15

1. Introduction
Embodied cognition is a theoretical concept that assumes cognitive functions areclosely related to the body and physical behavior [1,2]. In traditional cognitive modelexperiments, such as symbolic mental representations governed by logical and computational rules, the body only responds to information received by the senses. However,embodied cognition argues that the physicality of the body in action is not merely a vehicle
for logical and computational process, but also the co-producer of the cognitive process [1].
In addition, the body reflects the state of the mind to some extent. For example, hand
movements play a cognitive role in language development when speaking, while gesturesand finger counting also help to express mathematical concepts. Embodied cognition isa highly flexible and complex mechanism, and it is difficult to explain this phenomenon
with a single experiment, but it could be observed through experiments on cognition andphysical behavior [1,3].
The theory of embodied cognition indicates that perception is based on the state
and actions of the body, but research has also shown that there was no simple boundary
between perception and action [4]. The cognitive state of the mind and the behavioral
response of the body are the issues discussed in many studies [5]. Although psychology
affects the body’s response, the degree of its influence is difficult to quantify throughexperiments. However, Gregg D’s [6] research showed that the change of mental state
and the connection of the body and mind could be measured by the spectrum analysis ofelectroencephalographic (EEG) signal. For researchers studying situated cognition andsensorimotor function, embodied cognition is increasingly accepted as a viable theoreticalchoice [5,7,8].
Humans’ attention while focusing on a specific target would affect the neural activitiesof the human brain. Experiments designed with simple symbols, such as Go/No-Go
tasks [9,10] and attention network tasks (ANT) [11–13] have been used in the study of mind–
attention cognition tasks to investigate the neural activities of embodied mind cognition
states. Although the neural activity of embodied mind cognition states can be understood
through simple symbol experimental design, in the real world, focusing on a specific target
is more complex than during an experiment in a laboratory. However, previous studies did
not use a real-world environmental setting for the participants. Hence, the development ofeffective methods for measuring neural activities during mind–attention cognition tasks inreal-world environments is critical. In the present study, we designed a novel embodied
cognitive target detection experiment based on a real-world military scenario to explore thebrain’s dynamic changes in the process of the mind and body’s response under a real-world
setting target detection task.
This study is the first to adopt high-temporal-resolution wireless EEG recordings thatare more suitable in the real word [14] for investigating the brain activities of individuals
performing embodied mind cognition tasks in a military scenario by using independent
component analysis (ICA) [15] and dipole source localization [16]. In addition, to further
explore the interaction between the subject’s mental cognitive process and behavioral
response under different commands and tasks, our experiment was designed for the participant to execute corresponding tasks after each command was issued, as compared withtraditional ANT experiment settings with no-cue design [17,18]. The event-related potential(ERP) and event-related spectral perturbation (ERSP) techniques were used to investigate
the neural activities of the brain when the participants were performing embodied mind
cognition tasks under the experiment based on the military scenario. The ERSP reflects
an event-related time/frequency state within the EEG signals and elucidates the extent
to which underlying event-related synchronization or event-related desynchronization
occurs [19]. Consequently, ICA and ERSP analysis provides a direct measure of the changes
in the activity of the brain [15,19–21].

Sensors 2021, 21, 5213

3 of 15

2. Materials and Methods
2.1. Participants
A total of 19 healthy participants (15 men and 4 women, aged 23 to 30 years) wererecruited in this study [22,23]. All the participants were right-handed and had normal orcorrected vision without a history of neurological or psychiatric diseases. This study was
performed in accordance with the recommendations of the Institutional Review Board of
National Chiao Tung University, Hsinchu, Taiwan, and was approved by the Research
Ethics Committee of National Chiao Tung University, Hsinchu, Taiwan, under the protocol
code NCTU-REC-108-085E. Informed consent included options to exclude these diseases
and problems. Each participant had to sign the informed consent before the experiment.
2.2. Procedures
2.2.1. Experimental Design
The experiment was designed to simulate a real-world military shooting scenario
using Unity (2018.3.6 version) software. Unity is a platform game engine developed
by Unity Technologies, which creates three-dimensional (3D) and two-dimensional (2D)
games, as well as interactive simulations and other experiences [24]. We used Unity todesign the scenario and record the event mark and behavior data. The task scenario
simulates the real environment for target search, and the real target was the image of asoldier wearing camouflage clothing with a black gun, and the interference object was
the image of other characters wearing clothes of similar colors composed of gray, green,khaki, and black. As shown in Figure 1, the total duration of the experiment was 68.5 min,divided into 30 s of baseline (resting EEG). This was followed by 12 min sessions withaSensors 2021, 21, x FOR PEER REVIEW
4 of 15
5 min break between each session, and then followed by the next session with a total offour sessions and four breaks.

FigureExperimental
designthesimulated
real-world
targetdetection
task
based
a military
Figure1. 1.
Experimental
designofofthesimulated
real-world
targetdetection
task
based
onon
a militaryscenario. (a) Schematic flow of the experiment. (b) Nine types of targets. (c) Response buttons on
scenario. (a) Schematic flow of the experiment. (b) Nine types of targets. (c) Response buttons on thethe keyboard. (d) Three different conditions.
keyboard. (d) Three different conditions.

2.2.2.
EEG
Acquisition
anda PreprocessingEachsessionincluded
total of 180 trials. In each trial, a fixation cross appeared in
the center
the screen
the time.from
After
durationparticipants(1400 ms), ausing
red asteriskcommand
EEGofsignals
wereallcollected
alla healthy
a wireless
EEG cap
symbolon the inleftFigureor right
of TM
theVegafixationcross in the center
ofVega, as shown2. sidesSt. EEGis manufactured
by Artisecalled appeared
St. EEGTMrandomly
thescreen for Co.,
150 ms.
After
450 ms,the targetdetachable
and interference
object were
presented,
Biomedical
Ltd.,
Taiwan.
It featureswater-based
spongesensors and aminiature amplifier, in which EEG signals were transmitted wirelessly through Bluetooth
at a sampling rate of 500 Hz. St. EEGTM Vega came with 35 sensors, including 32 recording
electrodes, A1 and A2 reference electrodes, and FPz ground electrodes. All the 32 recording channels were located at the positions used in the International 10–20 systems.

Sensors 2021, 21, 5213

4 of 15

the participant will search for the real target within 2 s and make an execution by pressing
the corresponding arrow button after judging the previous command. We recorded thetime from the appearance of the target until the subject executes the button as reaction
times (RT), as shown in Figure 1a. When the real target appeared on the left side, thesubject was supposed to press the “left” arrow button on the keyboard, whereas if thetarget appeared on the right side, the subject was supposed to press the “right” arrow
button on the keyboard. When no specified target appeared, the subject was supposed topress the “up” arrow button, as shown in Figure 1c. In addition to the real target, there wasother interfering targets. These targets with different characteristics formed a combinationof 9 scenarios and appeared randomly in different trials, as shown in Figure 1b.
The command symbol appeared on the left or right side randomly, but the directionin which the target will did not necessarily correspond to it. According to the directionin which the command appears and the target appears, three different conditions were
defined. When the command symbol and target appear on the same side, we define themas congruent conditions. Conversely, when the command symbol and target appear on
Figure 1. Experimental
designof thereal-world
targetIfdetection
task
based on symbols
a militarydifferent
sides, we definethem
as simulatedincongruent
conditions.
there arecommandscenario.
(a) Schematic
flowweof theexperiment.
(b) theNinenontarget
types of targets.
(c) Response
buttons inonbut
no target
appearing,definedthem asconditions,as displayed
the keyboard.
(d) Threeconditions.
Figure1d. These
threedifferent
conditions
appeared in the same proportion in each session.

2.2.2. EEG
EEG Acquisition
Acquisition andand Preprocessing
Preprocessing
2.2.2.
EEG signals
signals were
were collectedcollected fromfrom all
all healthy
healthy participants
participants using
using aa wirelesswireless EEG
EEG capcap
EEG
TM Vega, as
TM Vegaas shown
shown inin Figure
Figure2.2.St.
St. EEGEEGTM
Vega isis manufactured
manufactured bybyArtise
Artisecalled St.
St. EEG
EEGTM
calledBiomedical
Biomedical Co.,
Co., Ltd.,
Ltd., Taiwan.
Taiwan. ItIt featuresfeatures detachable
detachable water-based
water-based sponge
sponge sensors
sensors andand aaminiature
miniatureamplifier,amplifier,ininwhichwhichEEG
EEGsignals
signalswere
weretransmitted
transmittedwirelessly
wirelesslythroughthroughBluetoothBluetooth
TM Vegaat
Vega came with 35 sensors,
at aa sampling
samplingrate
rateofof500
500Hz.
Hz.St.
St. EEGEEGTM
sensors, including
including 32
32 recording
recordingelectrodes,
A1
and
A2
referenceelectrodes,
andFPzgroundelectrodes.
All
the
32
recordingelectrodes, A1 and A2 reference electrodes, and FPz ground electrodes. All the 32 recordchannels
werewerelocatedat theusedusedin the10–2010–20
systems.
ing channels
locatedatpositions
the positions
inInternational
the International
systems.

FigureFigure2.
2. TheThe wirelesswireless andandwearable
wearableEEG
EEGsystem
system(St.
(St. EEGTM
EEGTM Vega)
Vega)usedusedinindatadataacquisition.
acquisition.

All
All thethe EEGEEG signals
signals were
were evaluatedevaluated using
using MATLAB
MATLAB R2012b
R2012b (MathWorks(MathWorksInc.
Inc. Natick,
Natick,
MA,
USA)
and
EEGLAB
toolbox(10.2.2.4b
version)[16,20].
The
EEG
signals
were
MA, USA) and EEGLAB toolbox (10.2.2.4b version) [16,20]. The EEG signals weredown
downsampledsampled fromfrom 500
500to
to250
250Hz.
Hz. A
A 1–50
1–50 HzHz finite
finite impulse
impulseresponse
responsebandpass
bandpassfilter
filterwas
wasusedused
to filter the EEG signals. ICA was performed to eliminate various artifacts, including
muscle artifacts, eye movements, blinking artifacts, noise from indoor power lines, andenvironmental artifacts. ICA is an excellent computational method for the separation ofblind sources in EEG signals [15,25]. An EEGLAB toolbox (10.2.2.4b version) and MATLAB
R2012b (MathWorks Inc.) were used to perform ICA [16]. After performing ICA, EEG
dipole source location analysis was conducted using DIPFIT2 routine functions of theSensors 2021, 21, 5213

to filter the EEG signals. ICA was performed to eliminate various artifacts, including muscle artifacts, eye movements, blinking artifacts, noise from indoor power lines, and environmental artifacts. ICA is an excellent computational method for the separation of blindsources in EEG signals [15,25]. An EEGLAB toolbox (10.2.2.4b version) and MATLAB
5 of 15
R2012b (MathWorks Inc.) were used to perform ICA [16]. After performing ICA, EEG dipole source location analysis was conducted using DIPFIT2 routine functions of the EEGLAB toolbox (10.2.2.4b version) [15,16,25]. We then performed k-means clustering (K =EEGLAB(10.2.2.4b
version) [15,16,25].
We then performed
k-means5)
analysistoolbox
using theaforementioned
toolbox. Components
with similar
scalp clusteringmaps, di(K=5)
analysisusing
theaforementioned
toolbox.
Components
withsimilarmaps,
pole locations, and power spectra were grouped in a cluster. We observed threescalp
consistent
dipolelocations,
and
power
spectraweregroupedinacluster.
Weobserved
threeconsistent
independent component scalp maps and dipole clusters, namely, right frontal lobe, left
independentmaps
anddipolenamely,right thesefrontallobe,clusleft
motor
cortex, component
and middle scalp
occipital
lobe,
from
the clusters,
participants.
We usedthree
motor
cortex,andmiddleoccipital
lobe,
from
the
participants.
We
used
these
three
clusters
ters as the brain regions of interest to investigate the brain dynamics, in terms of ERP andas the responsible
brain regionsfor
oftheinterest
to investigate
thethebrain
dynamics,
in terms
ERP
and ERSP,
ERSP,
interactions
betweenmind
and body,
whileofthe
participants
responsiblefortheinteractions
between
the
mind
and
body,
while
the
participants
were
were performing the embodied cognition experiment in the military scenario [16]. Accordperformingtheembodied
cognition
experimentinthemilitary
scenario[16].
Accordingto
ing to the event marker in EEG signals, each epoch was extracted from −500 to 2600 ms in
the
event
markerinEEGsignals,
each
epoch
was
extracted
from
−
500
to
2600
ms
in
the
the congruent, incongruent, and nontarget trials. In this study, each participant performed
congruent, incongruent, and nontarget trials. In this study, each participant performed
720 trials under three conditions (congruent: 240 trials, incongruent: 240 trials, and non720 trials under three conditions (congruent: 240 trials, incongruent: 240 trials, and nontartarget: 240 trials). During the artifact removal procedure, poor-quality raw EEG signals
get: 240 trials). During the artifact removal procedure, poor-quality raw EEG signals were
were obtained in approximately 10% of the trials. Therefore, 10% of the EEG trials wereobtained in approximately 10% of the trials. Therefore, 10% of the EEG trials were removed
removed to obtain clean EEG signals for ERP and ERSP analysis. Figure 3 displays theto obtain clean EEG signals for ERP and ERSP analysis. Figure 3 displays the flowchart offlowchart of the EEG signal analysis under the congruent, incongruent, and nontarget
the EEG signal analysis under the congruent, incongruent, and nontarget conditions.
conditions.

Figure 3. Flowchart of EEG signal analysis and preprocessing steps of acquired EEG signals.
Figure 3. Flowchart of EEG signal analysis and preprocessing steps of acquired EEG signals.

2.3.
2.3. Statistical
Statistical Analyses
AnalysesThe
behavioral
The behavioral data
data were
were collectedcollected using
using Unity
Unity software
software andand were
were processed
processed using
using
MATLAB.
For
eachparticipant,we
categorized
theresponse
executiontime
correspondMATLAB. For each participant, we categorized the response execution time correspondinging
to thethreedifferent
trial
conditions(asininFigure
Figure1d)1d)according
according toto thethe eventto thethreedifferent
trial
conditions(as
event markers.
markers.
Finally,
wecalculatedtheaverageandstandard
deviationofthe19
subjects
Finally, we calculated the average and standard deviation of the 19 subjects under
under three
three
conditions.
We usedusedone-way
one-wayanalysis
analysis
variance(ANOVA)
multiplecomparison
conditions. We
ofofvariance(ANOVA)
andandmultiplecomparison
tests
tests
to determine
whetherthe differencesamong
the congruent,incongruent,and nonto determine
whetherthe differencesamong
the congruent,incongruent,and nontarget
targettrialssignificant
were significant(p < 0.05).
trials were(p < 0.05).
In
the
EEG
signalanalysis,
In the EEG signal analysis, this
this study
study investigatedinvestigated thethe neuralneural dynamics
dynamics ofof the rightfrontal
lobe,
left
motor
cortex,andmiddleoccipital
lobe
under
congruent,incongruent,frontal lobe, left motor cortex, and middle occipital lobe under congruent, incongruent,andand nontarget
nontarget conditions
conditions inin thethe military-based
military-based embodied
embodied cognition scenario. ERP
ERP analysis
analysiswaswas performed
performed toto determine
determine thethe statisticallystatistically (p << 0.01)
0.01) significant
significant differences
differences among
among thecongruent, incongruent, and nontarget conditions, based on the Wilcoxon signed-rank test.
The yellow asterisks indicate pairwise significance between the congruent and incongruent
conditions. Violet asterisks display pairwise significance between the incongruent andnontarget conditions. Green asterisks reveal pairwise significance between the congruent
and nontarget. In the ERSP analysis, the statistically significant differences in the time andfrequency domains were evaluated using the bootstrap method [16,26] at a significanceSensors 2021, 21, 5213

6 of 15threshold of p < 0.05 under congruent, incongruent, and nontarget trials over the rightfrontal lobe, left motor cortex, and middle occipital lobe. Furthermore, the average power
spectral density (PSD) of EEG signals (1–50 Hz) was visualized under the congruent,incongruent, and nontarget conditions in the right frontal lobe, left motor cortex, andmiddle occipital lobe. Asterisks indicate a significant difference among the congruent,incongruent, and nontarget conditions in the Wilcoxon signed-rank test (* p < 0.05).
3. Results
3.1. Behavioral Results
The significant difference in the RT between various conditions (the congruent, incongruent, and nontarget conditions) was calculated, as presented in Figure 4 and Table 1.
Table 1 lists the RT of the participants under the aforementioned three conditions. The
average RT was 582 ± 89, 666 ± 102, and 863 ± 158 ms under the congruent, incongruent,and nontarget conditions, respectively. We used ANOVA1 and post hoc comparisons toanalyze the significant differences in RTs among the three conditions. ANOVA1 revealed
significant differences in the RTs for the three conditions (F (2, 53) = 5.07, p < 0.01), andpost hoc comparisons indicated that the RT under the congruent condition was significantly shorter than those under the incongruent and nontarget conditions (p < 0.05 andp < 0.01, respectively), as displayed in Figure 4. The RT under the incongruent condition
was significantly shorter than that under the nontarget condition (p < 0.01), as illustrated in
Figure 4.
Table 1. RT of the participants, including the average and standard deviation of the RT under the
congruent, incongruent, and nontarget conditions.
Subjects

Congruent
RT (ms)Incongruent
RT (ms)

Nontarget
RT (ms)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Avg ± SD

635
696
454
492
551
665
535
600
456
580
486
645
622
501
759
584
547
655
598
582 ± 89

725
753
665
535
697
704
631
799
648
600
537
630
693
614
851
637
584
757
592
666 ± 102

887
1030
830
723
991
902
716
983
766
664
754
843
842
813
1199
950
816
944
744
863 ± 158

Sensors 2021, 21, x FOR PEER REVIEW
Sensors 2021, 21, 5213

7 of 15
7 of 15

Figure4.4.Differences
Differencesininthe
theRT
RTunderunderthethecongruent,congruent,incongruent,incongruent,and
andnontarget
nontargetconditions
conditionsunder
under
Figurethesimulated
real-world
targetdetection
task.
The
error
bars
denotethestandard
deviation.
The
the simulated real-world target detection task. The error bars denote the standard deviation. The
asterisksdenotethesignificancedifferences
(*
p
<
0.05,
***
p
<
0.01)
in
the
RT
under
the
three
condiasterisks denote the significance differences (* p < 0.05, *** p < 0.01) in the RT under the threetions obtained using ANOVA1.
conditions obtained using ANOVA1.

3.2. EEG Results
3.2. EEG Results
3.2.1.ICA
ICAScalp
ScalpMaps
MapsandandDipole
DipoleSource
SourceLocations
Locationsunder
UnderTarget
targetdetection
detection
3.2.1.
ICAwaswasperformed
performedtotoisolate
isolatethe
theEEG
EEGsignals
signalsofofindependent
independentbrain
brainregions
regionsfrom
fromICA
noise,
suchaseye
activities
andmuscleactivities
thattypicallyaffecttheanalysisofEEGnoise, such as eye activities and muscle activities that typically affect the analysis of EEG
signals. After
DIPFIT2
function
to fitdipolesin EEGsignals.
After conducting
conductingICA,
ICA,weweadopted
adoptedthe
the
DIPFIT2
functionto thefit thedipolesinsignals
by using
the EEGLAB
Toolboxand and
MATLAB[16,27,28].
The TheEEGEEG
dipoleswereEEG
signals
by using
the EEGLAB
Toolbox
MATLAB[16,27,28].
dipolesclusteredusingusing
the k-means
statisticalanalysis
criteria(k = 3)The valueof k waswereclusteredthe k-means
statisticalanalysis
criteria(k[16,20].
= 3) [16,20].
The valueofacquiredbyconsideringthepotential
numberofdipolesthat
wererelatedtothek was acquired by considering the potential number of dipoles that were relatedcongruto theent, incongruent,and nontarget
conditions.
Furthermore,similarsimilar
scalp maps
and dipole
congruent,incongruent,and nontarget
conditions.
Furthermore,
scalp maps
andlocationswere clusteredinto thesamefor allfor
theallparticipants,
as depicted
in Figuredipolelocationswere clusteredinto
the group
same groupthe participants,
as depicted
in
5. All 5.
three
and theMontreal
Neurological
Institutecoordinatesof theirFigureAll clusters
three clusters
andthe Montreal
Neurological
Institutecoordinatesof dipoletheirsourcesource
locationsare presented
in Table
These
brain
directeddirected
the ERP theanddipolelocationsare presented
in2.Table
2. threeThesethreeregions
brain regions
ERSP
analysisof EEG signals
with thewith
embodied
mind cognition
state changes
ERP
and
ERSP analysisof EEGassociatedsignals associatedthe embodied
mind cognition
state
in the realistic
militarymilitaryscenario,as describedin the following
sections.
changesin the realistic
scenario,as describedin the following
sections.
Table 2. Three independent component clusters in the brain and the Montreal Neurological Institute coordinates of their dipole source locations under the congruent, incongruent, and nontarget
conditions.
Component
Clusters

Side

Brain Regions

1
2
3

Right
Left
MiddleFrontal Lobe
Motor Cortex
Occipital Lobe

MNI: Montreal Neurological Institute.

MNI Coordinates (mm)X

YZ

Cluster Size
(Voxels)

7
−9
−2

57
−26
−80

−13
54
39

6
9
12

Sensors 2021, 21, 5213

acquired by considering the potential number of dipoles that were related to the congruent, incongruent, and nontarget conditions. Furthermore, similar scalp maps and dipole
locations were clustered into the same group for all the participants, as depicted in Figure
5. All three clusters and the Montreal Neurological Institute coordinates of their dipole
source locations are presented in Table 2. These three brain regions directed the ERP
8 ofand15
ERSP analysis of EEG signals associated with the embodied mind cognition state changes
in the realistic military scenario, as described in the following sections.

Figure 5. Three clusters of independent component scalp maps and dipole source locations at theright frontal lobe, left motor cortex, and middle occipital lobe of all the participants under the
congruent, incongruent, and nontarget conditions. Each cluster reveals the average scalp maps anddipole locations for all the independent components within it. Cluster 1: right frontal lobe (N = 19).
Cluster 2: left motor cortex (N = 19). Cluster 3: middle occipital lobe (N = 19), where N is the numberof diploes projected in a cluster.

3.2.2. ERP Analysis
ERP N500 and P300 Waves in the Right Frontal Lobe
The average ERP under the congruent, incongruent, and nontarget conditions at theright frontal lobe, left motor cortex, and middle occipital lobe of the brain are displayedin Figure 6. Peaks of the human visual-attention-related neural markers ERP N500 andP300 were observed in the right frontal lobe and left motor cortex under the congruent,incongruent, and nontarget conditions. In the right frontal lobe and left motor cortex of thebrain, the amplitudes of the ERP N500 and P300 waves were considerably higher under
the congruent and incongruent conditions than under the nontarget condition. In addition,in the right frontal lobe, the ERP N500 and P300 waves appear earlier under consistentand inconsistent conditions than under nontarget conditions. The aforementioned results
revealed that, compared with nontarget conditions, participants detected the target earlier and caused stronger neural dynamics in the right frontal lobe in the congruent andincongruent conditions, while under nontarget conditions, the subject’s brain takes longer
to detect the situation where no target image appears, and relatively showed weak nerve
activity in the right frontal lobe. Furthermore, the ERP N200 and P300 waves were observedin the middle occipital lobe under the congruent, incongruent, and nontarget conditions.
However, the amplitudes of these waves were higher in the right frontal lobe than in theleft motor cortex and middle occipital lobe. The aforementioned ERP results indicate thatthe right frontal lobe is highly related to an embodied mind cognitive attention state. These
ERP results show that body function influences human mental states.

Sensors 2021, 21, 5213
Sensors 2021, 21, x FOR PEER REVIEW

9 of 15
9 of 15

Figure 6. Average ERP under the congruent, incongruent, and nontarget conditions at the right
Figure 6. Average ERP under the congruent, incongruent, and nontarget conditions at the right frontal
frontal lobe, left motor cortex, and middle occipital lobe. Yellow asterisks indicate pairwise signifilobe, left motor cortex, and middle occipital lobe. Yellow asterisks indicate pairwise significance
cance (* p < 0.01) between the congruent and incongruent conditions in the Wilcoxon signed-rank(* ptest.
< 0.01)
betweenthedenotecongruentandsignificanceincongruent
in the theWilcoxon
signed-rank
test.
Violetasterisks
pairwise(* conditions
p < 0.01) between
incongruent
and nontarget
Violetasterisksdenotepairwisesignificance
(*
p
<
0.01)
betweentheincongruent
andnontarget
conditions in the Wilcoxon signed-rank test. Green asterisks denote pairwise significance (* p < 0.01)
conditions
theWilcoxonand
signed-rank
Green asterisks
denote pairwise
significance(* p < 0.01)
betweeninthecongruentnontargettest.
conditionsin the Wilcoxon
signed-rank
test.
between the congruent and nontarget conditions in the Wilcoxon signed-rank test.

3.2.3. ERSP Analysis
3.2.3. ERSP Analysis
Delta
and
Theta
Power
Suppression
in inthetheRight
Frontal
Lobe
Delta
and
Theta
Power
Suppression
Right
FrontalLobe
Theaverage
ERSP
and nontarget
nontargetconditions
conditionsinintheTheaverage
ERSPunder
underthethecongruent,congruent, incongruent,incongruent, andtheright
rightfrontal
frontallobe,
lobe, left
left motor
motor cortex,
andmiddleoccipital
lobe
of
the
participants
arearecortex, and middle occipital lobe of the participants
shown in Figure 7. For the right frontal lobe, the embodied cognition attention-related EEG
shown in Figure 7. For the right frontal lobe, the embodied cognition attention-related
activity of delta (1–4 Hz), theta (4–7 Hz), and alpha (8–12 Hz) powers from 600 to 1200 ms
EEG activity of delta (1–4 Hz), theta (4–7 Hz), and alpha (8–12 Hz) powers from 600 towere lower under the congruent and incongruent conditions than under the nontarget
1200 ms were lower under the congruent and incongruent conditions than under the nonconditions. However, after hand response, in the right frontal lobe, the movement of thetarget conditions. However, after hand response, in the right frontal lobe, the movement
hand-related EEG activity in the delta (1–4 Hz), theta (4–7 Hz), and alpha (8–12 Hz) bandof the hand-related EEG activity in the delta (1–4 Hz), theta (4–7 Hz), and alpha (8–12 Hz)
powers from 1200 to 2600 ms was higher in the nontarget conditions than in the congruent
band powers from 1200 to 2600 ms was higher in the nontarget conditions than in theand incongruent conditions.
congruent and incongruent conditions.

Mu Rhythm Suppression in the Left Motor Cortex
Figure 7 also indicates that the alpha (8–12 Hz) and beta (13–30 Hz) band powers or
mu rhythms at the left motor cortex were suppressed to a greater extent in the nontarget
condition than in the congruent and incongruent conditions after target onset. This expected alpha and beta power suppression is associated with embodied cognition attention

state and hand movement. Figure 7 also indicates that the theta (4–7 Hz) band power at
the middle occipital lobe was higher in the nontarget condition than in the congruent andincongruent conditions after target onset. The beta power (13–30 Hz) at the occipital lobe
Sensors 2021, 21, 5213
was lower in the congruent condition than in the other conditions. The EEG power activity
changes were related to an embodied cognition attention state.

10 of 15

Figure 7.ERSP
Average
ERSP
at therightlobe,
frontalleftcortex,
motor cortex,
and middle
occipital
lobe under
Figure 7. Averageat theright
frontalleftlobe,
motor
and middle
occipital
lobe under
the congruent,
the congruent, incongruent, and nontarget conditions. The first magenta line denotes the commandincongruent, and nontarget conditions. The first magenta line denotes the command onset. The second black dashed line
onset. The second black dashed line reveals the target. The third black dashed line represents thereveals the target. The third black dashed line represents the response execution after target detection. The ERSP difference
response execution after target detection. The ERSP difference is significant at p < 0.05. The coloredis significantbars
at pindicate< 0.05. The
indicate the scale of ERSP.
the colored
scale of bars
ERSP.

Rhythm Suppression in the Left Motor Cortex
3.2.4. EEG PSDMu
Analysis
Figure7 alsoindicates
theright
alpha(8–12lobe,Hz) andbeta (13–30
Hz)band powers or
The average PSDof theEEG
signals thatin thefrontalleft motor
cortex,andmurhythms
at
the
left
motor
cortexwere
suppressedtoagreater
extentinmiddle occipital lobe of the participants under the congruent, incongruent, and nontarget the nontarget
in the8.congruent
andincongruent
after
targetconditions arecondition
displayedthanin FigureAt the rightfrontallobe, theconditions
hand-related
EEG
PSDonset. This expected
alpha
and
beta
power
suppressionisassociatedwithembodied
cognition
attention
activity in the delta (1–4 Hz), theta (4–7 Hz), and alpha (8–12 Hz) band powers was higher
state
and
hand
movement.
Figure7also
indicates
that
the
theta
(4–7
Hz)
band
power
at
in the nontarget condition than in the congruent and incongruent conditions after hand
the middle occipital lobe was higher in the nontarget condition than in the congruent andresponse. Figure 8 indicates that the alpha (8–12 Hz) and beta (13–30 Hz) band powers atincongruent conditions after target onset. The beta power (13–30 Hz) at the occipital lobe
the left motor cortex were suppressed to a greater extent in the nontarget condition than
was lower in the congruent condition than in the other conditions. The EEG power activity
in the congruent and incongruent conditions. The aforementioned findings are related tochanges were related to an embodied cognition attention state.
the movement of the right hand. These PSD results show that embodied body function
influences humanstates.
Figure 8 also indicates that the theta (4–7 Hz) band power
3.2.4.mental
EEG PSD
Analysisat the middle occipital lobe was higher in the nontarget condition than in the congruent
The average PSD of the EEG signals in the right frontal lobe, left motor cortex, andand incongruent conditions. This result is attributed to the cognition visual stimulation in
middle occipital lobe of the participants under the congruent, incongruent, and nontarget
the occipital lobe.
conditions are displayed in Figure 8. At the right frontal lobe, the hand-related EEG PSD
activity in the delta (1–4 Hz), theta (4–7 Hz), and alpha (8–12 Hz) band powers was higher
in the nontarget condition than in the congruent and incongruent conditions after hand
response. Figure 8 indicates that the alpha (8–12 Hz) and beta (13–30 Hz) band powers at
the left motor cortex were suppressed to a greater extent in the nontarget condition thanin the congruent and incongruent conditions. The aforementioned findings are related tothe movement of the right hand. These PSD results show that embodied body function
influences human mental states. Figure 8 also indicates that the theta (4–7 Hz) band powerat the middle occipital lobe was higher in the nontarget condition than in the congruent
and incongruent conditions. This result is attributed to the cognition visual stimulation in
the occipital lobe.

Sensors 2021, 21, 5213
Sensors 2021, 21, x FOR PEER REVIEW

11 of 15
11 of 15

Figure 8. Average PSD of EEG signals at the right frontal lobe, left motor cortex, and middle occipital
Figure 8. Average PSD of EEG signals at the right frontal lobe, left motor cortex, and middle occipital
lobe under the congruent, incongruent, and nontarget conditions. Asterisks denote the significant
lobe under the congruent, incongruent, and nontarget conditions. Asterisks denote the significant
differences among the congruent, incongruent, and nontarget conditions in the Wilcoxon signeddifferences
congruent, incongruent, and nontarget conditions in the Wilcoxon signed-rank
rank testamong(*** p <the0.05).
test (*** p < 0.05).

4. Discussion
4. Discussion
present
study,
useda wirelessEEGsystemto investigate
neuralactivities
In In
thethepresent
study,
wewe
used
a wirelessEEGsystemto investigate
neuralactivities
when
performing
embodiedmind
cognition
tasks
in
a
military
scenariothroughwhen performing embodied mind cognition tasks in a military scenario throughICA
ICAanddipolesourcelocationanalysis.
High-temporal-resolution
EEG
signalswereused
to oband dipole source location analysis. High-temporal-resolution EEG signals were used
serve thethe
neuralmarkersof embodied
cognitionin thefrontallobe,lobe,
left motor
cortex,to observe
neuralmarkersof embodied
cognitionin right
the right
frontalleft motor
andmiddleoccipital
lobe
under
congruent,incongruent,andnontarget
conditions
cortex, and middle occipital lobe under congruent, incongruent, and nontarget conditionsin a
ByBy
combining
behavioral
methods
and
ICA,
this
study
identified
thetheneuin amilitary
militarysetting.
setting.
combining
behavioral
methods
and
ICA,
this
study
identifiedralEEG
markersofembodied
cognitive
attentionstatesintherightfrontal
lobe,
left
motor
neural EEG markers of embodied cognitive attention states in the right frontal lobe, left
cortex,and andmiddleoccipital
lobe.lobe.
motor
cortex,
middleoccipital
4.1.4.1.
Behavior
Outcomes
When
Performing
Embodied
Cognition
Tasks
Behavior
Outcomes
When
Performing
Embodied
Cognition
Tasks
The
RTRT
in thecongruentconditionwasshorterthan
thosein theincongruent
andandThein thecongruentconditionwasshorterthan
thosein theincongruent
nontarget
conditions(Figure4). This
resultrevealsthat that
all thepaid paid
moremore
focusfonontarget
conditions(Figure4). This
resultreveals
allparticipants
the participants
to searchingfor a realcongruentcondition
than under
cus to searching
fortargeta real undertarget theunder
the congruent
condition
than other
underconditions.
other condiWetions.
compared
the results
under thecongruentand incongruent
conditions
with
We compared
theobtained
results obtained
under
the congruent
and incongruent
conditions
thoseof thosea previous
study onstudy
cognition
attention attention
tasks [29].tasks
The aforementioned
behavioralbewithof a previous
on cognition[29]. The aforementioned
findings
arefindings
consistent
with thosewithof a thoseprevious
study onstudy
cognition
attention
tasks [29].
havioralare consistentof a previous
on cognition
attention
tasks

Sensors 2021, 21, 5213

12 of 15

In this study, in the nontarget condition, all the participants exhibited a long RT because
they exerted some effort in searching for the target (soldier with a gun). This finding
revealed that human mental state influenced body function and performance in cognition
attention-related tasks.
4.2. EEG–ERP N500 and P300 Waves in the Right Frontal Lobe
In this study, peaks of the embodied cognitive attention-related EEG neural markers
ERP N200, N500, and P300 were detected in the right frontal lobe and left motor cortex
under the congruent, incongruent, and nontarget conditions (Figure 6). These ERP waves
had a higher amplitude in the congruent and incongruent conditions than in the nontarget
condition. The ERP N500 and P300 waves had a higher amplitude in the right frontal
lobe than in the left motor cortex and middle occipital lobe. These ERP neural markers
revealed that the right frontal lobe was related to embodied cognition attention. Most
previous cognition attention studies have reported N200 and P300 waves in the frontal
lobe [11,30–33]. Furthermore, ERP-N200 and P300 waves have been proven to be related
to cognition attention in the frontal lobe of the brain. Furthermore, ERP-N200 and P300
waves have been examined under human inhibitory control [30,34]. N500 was previously
found to be related to the semantic incongruities in audio perception [35]. In this study, forthe first time, we investigated the ERP neural markers of the frontal lobe (N500), left motor
cortex (N500 and P300), and middle occipital lobe (N200 and P300) of the brain. The ERP
neural markers and short behavioral RTs under the congruent condition revealed that all
the participants were more likely to pay attention to the target appearing in the congruent
condition of a military scenario. Additionally, the subjects need to spend more time andthinking when the command and the execution of the task are not in conformity. TheseERP findings show that embodied body function influences mental states under cognition
attention-related tasks.
4.3. Delta, Theta, and Alpha Power Suppression in the Right Frontal Lobe
The average ERSP results indicated that delta, theta, and alpha power suppression
occurred in the right frontal lobe, as displayed in Figure 7. The cognition attention-related
EEG neural activity of the delta, theta, and alpha band powers were lower in the congruent
condition than in the incongruent and nontarget conditions. Cognition attention-related
studies have revealed that theta and alpha band power suppression is related to humancognition attention in the frontal lobe of the brain [36–39]. Furthermore, in the right frontal
lobe of the brain, the movement of the hand-related EEG powers of the delta, theta, andalpha bands from 1200 to 2600 ms were higher in the nontarget condition than in the
congruent and incongruent conditions. The aforementioned ERSP findings reveal that thefrontal lobe is related to the hand response and cognition attention.
4.4. Alpha and Beta Power Suppression in the Left Motor Cortex
We measured the spectral changes in the EEG power, such as the suppression ofalpha and beta powers, at the left motor cortex (Figure 8). Alpha power suppression wasconsiderably higher in the nontarget condition than in the congruent and incongruent
conditions. This result reveals that the left motor cortex is related to cognition attention
and hand movement. Moreover, the alpha and beta band power suppression in the motor
cortex is consistent with the results of previous studies conducted on healthy individuals
under walking conditions [40–43]. The theta band power was considerably higher in
the nontarget condition than in the congruent and incongruent conditions in the middle
occipital lobe, and the beta power in the middle occipital lobe was lower than that in theother examined brain regions (Figure 8). A classroom attention study reported an increasein the delta and theta EEG powers in the occipital lobe and a decrease in the beta power inthe occipital lobe [44]. These results reveal that the occipital lobe is related to an embodied
cognition attention task and visual stimulation under a military scenario.

Sensors 2021, 21, 5213

13 of 15

4.5. The Limitations of This Approach
Some of the previous studies explored how aging affects the influence of embodimenton mental representations. They examined age-related differences in mental imagery,motor imagery, and action observation [45,46]. The limitation of the present study is that
normal university students have participated in this experiment. In future work, we will
invite children and older adults to extend our research. Secondly, the experimental scene
in the current study was the simulated real-world target detection task based on a militaryscenario, which uses only two-dimensional figures and may not be the most realistic
setting for our subjects. Future work may construct an even more realistic environment in
virtual reality (VR) to increase the impact of target searching. Designing a new scenario for
imaging the human brain during target searching, such as in a virtual reality environment,
will provide further insight regarding embodied cognition.
5. Conclusions
This study is the first to use a wireless EEG system to investigate the brain dynamics ofhuman attention in an embodied military scenario by using ICA and dipole source location
analysis. In this study, considerable brain activity features stemming from cognition andattention had been obtained from a target detection task in a novel military scenario.
The behavioral results of this study revealed that the RT in the congruent condition wasconsiderably shorter than those in the incongruent and nontarget conditions. Furthermore,
the EEG results revealed the existence of three human attention-related ERP markers,
namely, ERP-N200, N500, and P300, in the right frontal lobe, left motor cortex, and middle
occipital lobe of the brain. We examined the embodied attention-related power spectralchange, including the delta and theta power suppression in the right frontal lobe, as wellas the alpha and beta power suppression in the left motor cortex in an attention task. The
theta power was higher in the middle occipital lobe than in the other brain regions. Ourfindings demonstrate the feasibility of studying human target detection brain activity in asimulated real-world condition. In addition, we detected the presence of the N500 marker
in a visual-based cognitive study. The obtained EEG results can be useful for researchers
to understand the interaction between the brain and human behavior in multiple target
detection tasks.
Author Contributions: Conceptualization, L.-W.K. and C.-L.Y.; formal analysis, C.H. and R.K.C.;
investigation, C.H. and R.K.C.; writing—original draft preparation, C.H. and R.K.C.; writing—review
and editing, L.-W.K.; visualization, C.H. and R.K.C.; supervision, L.-W.K.; funding acquisition,
L.-W.K. and C.-L.Y. All authors have read and agreed to the published version of the manuscript.
Funding: This work was supported by the National Chung-Shan Institute of Science and Technology,
under the contract XC08208PA59PE-CS. It was also supported by the Ministry of Science and Technology (MOST) under Grants 108-2221-E-009-045-MY3, 110-2622-E-A49-009-, and 110-2321-B-010-005-,
in part by the Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2 B) from the Featured
Areas Research Center Program within the framework of the Higher Education Sprout Project by the
National Yang Ming Chiao Tung University and Ministry of Education (MOE) in Taiwan.
Institutional Review Board Statement: The study was conducted according to the guidelines of the
Declaration of Helsinki and approved by the Institutional Review Board of National Chiao Tung
University (protocol code NCTU-REC-108-085E and date of approval is 19 November 2019).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The EEG datasets generated for this study are available on request to
the corresponding author.
Acknowledgments: The authors would like to thank Bo-Yu Tsai and Yang Chang for helping in thedevelopment of the experimental scenario, and all the subjects for joining this experiment.
Conflicts of Interest: The authors declare no conflict of interest.

Sensors 2021, 21, 5213

14 of 15

References
1.
2.
3.
4.
5.

6.
7.
8.
9.
10.
11.
12.
13.
14.

15.
16.
17.
18.
19.
20.
21.
22.
23.

24.

25.
26.
27.
28.
29.

Costello, M.C.; Bloesch, E.K. Are older adults less embodied? A review of age effects through the lens of embodied cognition.
Front. Psychol. 2017, 8, 267. [CrossRef]
Goldman, A.I. A moderate approach to embodied cognitive science. Rev. Philos. Psychol. 2012, 3, 71–88. [CrossRef]
Foglia, L.; Wilson, R.A. Embodied cognition. Wiley Interdiscip. Rev. Cogn. Sci. 2013, 4, 319–325. [CrossRef] [PubMed]
Creem-Regehr, S.H.; Kunz, B.R. Perception and action. Wiley Interdiscip. Rev. Cogn. Sci. 2010, 1, 800–810. [CrossRef] [PubMed]
Babayan, A.; Erbey, M.; Kumaral, D.; Reinelt, J.D.; Reiter, A.M.F.; Robbig, J.; Schaare, H.L.; Uhlig, M.; Anwander, A.; Bazin, P.-L.;
et al. A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults. Sci. Data
2019, 6, 1–21. [CrossRef] [PubMed]
Jacobs, G.D. The physiology of mind–body interactions: The stress response and the relaxation response. J. Altern. Complement.
Med. 2001, 7, 83–92. [CrossRef]
Glenberg, A.M.; Witt, J.K.; Metcalfe, J. From the revolution to embodiment: 25 years of cognitive psychology. Perspect. Psychol.
Sci. 2013, 8, 573–585. [CrossRef]
Barsalou, L.W. Situated conceptualization: Theory and applications. In Foundations of Embodied Cognition: Perceptual and Emotional
Embodiment; Coello, Y., Fischer, M.H., Eds.; Psychology Press: East Sussex, UK, 2016; pp. 11–37.
Mudar, R.A.; Chiang, H.-S.; Maguire, M.J.; Spence, J.S.; Eroh, J.; Kraut, M.A.; Hart, J., Jr. Effects of age on cognitive control during
semantic categorization. Behav. Brain Res. 2015, 287, 285–293. [CrossRef]
Bruin, K.; Wijers, A. Inhibition, response mode, and stimulus probability: A comparative event-related potential study. Clin.
Neurophysiol. 2002, 113, 1172–1182. [CrossRef]
Williams, R.S.; Biel, A.L.; Wegier, P.; Lapp, L.K.; Dyson, B.J.; Spaniol, J. Age differences in the Attention Network Test: Evidence
from behavior and event-related potentials. Brain Cogn. 2016, 102, 65–79. [CrossRef]
Causse, M.; Fabre, E.; Giraudet, L.; Gonzalez, M.; Peysakhovich, V. EEG/ERP as a measure of mental workload in a simple
piloting task. Procedia Manuf. 2015, 3, 5230–5236. [CrossRef]
Neuhaus, A.H.; Urbanek, C.; Opgen-Rhein, C.; Hahn, E.; Ta, T.M.T.; Koehler, S.; Gross, M.; Dettling, M. Event-related potentials
associated with Attention Network Test. Int. J. Psychophysiol. 2010, 76, 72–79. [CrossRef]
Lin, C.-T.; Ko, L.-W.; Chang, C.-J.; Wang, Y.-T.; Chung, C.-S.; Yang, F.-S.; Duann, J.-R.; Jung, T.-P.; Chiou, J.-C. Wearable andwireless brain-computer interface and its applications. In International Conference on Foundations of Augmented Cognition; Springer:
Berlin/Heidelberg, Germany, 2009; pp. 741–748.
Jung, T.-P.; Makeig, S.; McKeown, M.J.; Bell, A.J.; Lee, T.-W.; Sejnowski, T.J. Imaging brain dynamics using independent component
analysis. Proc. IEEE 2001, 89, 1107–1122. [CrossRef]
Delorme, A.; Makeig, S. EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent
component analysis. J. Neurosci. Methods 2004, 134, 9–21. [CrossRef]
Fan, J.; McCandliss, B.D.; Fossella, J.; Flombaum, J.I.; Posner, M.I. The activation of attentional networks. Neuroimage 2005, 26,
471–479. [CrossRef]
MacLeod, J.W.; Lawrence, M.A.; McConnell, M.M.; Eskes, G.A.; Klein, R.M.; Shore, D.I. Appraising the ANT: Psychometric andtheoretical considerations of the Attention Network Test. Neuropsychology 2010, 24, 637. [CrossRef]
Makeig, S.; Debener, S.; Onton, J.; Delorme, A. Mining event-related brain dynamics. Trends Cogn. Sci. 2004, 8, 204–210. [CrossRef][PubMed]Ko, L.-W.; Shih, Y.-C.; Chikara, R.K.; Chuang, Y.-T.; Chang, E.C. Neural mechanisms of inhibitory response in a battlefield
scenario: A simultaneous fMRI-EEG study. Front. Hum. Neurosci. 2016, 10, 185. [CrossRef]
Chikara, R.K.; Chang, E.C.; Lu, Y.-C.; Lin, D.-S.; Lin, C.-T.; Ko, L.-W. Monetary reward and punishment to response inhibition
modulate activation and synchronization within the inhibitory brain network. Front. Hum. Neurosci. 2018, 12, 27. [CrossRef]
Gang, L.; Hu, P.-P.; Jin, F.; Kai, W. Gender differences associated with orienting attentional networks in healthy subjects. Chin.
Med. J. 2013, 126, 2308–2312.
Matsuura, M.; Yamamoto, K.; Fukuzawa, H.; Okubo, Y.; Uesugi, H.; Moriiwa, M.; Kojima, T.; Shimazono, Y. Age development andsex differences of various EEG elements in healthy children and adults—quantification by a computerized wave form recognition
method. Electroencephalogr. Clin. Neurophysiol. 1985, 60, 394–406. [CrossRef]
Hussein, A.; García, F.; Olaverri-Monreal, C. Ros and unity based framework for intelligent vehicles control and simulation.
In Proceedings of the 2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES), Madrid, Spain, 12–14
September 2018; pp. 1–6.
Jung, T.-P.; Makeig, S.; Westerfield, M.; Townsend, J.; Courchesne, E.; Sejnowski, T.J. Removal of eye activity artifacts from visual
event-related potentials in normal and clinical subjects. Clin. Neurophysiol. 2000, 111, 1745–1758. [CrossRef]
Efron, B.; Tibshirani, R.J. An Introduction to the Bootstrap; CRC Press: Boca Raton, FL, USA, 1994.
Chikara, R.K.; Ko, L.-W. Neural Activities Classification of Human Inhibitory Control Using Hierarchical Model. Sensors 2019, 19,3791. [CrossRef]
Chikara, R.K.; Ko, L.-W. Modulation of the Visual to Auditory Human Inhibitory Brain Network: An EEG Dipole Source
Localization Study. Brain Sci. 2019, 9, 216. [CrossRef]
Fan, J.; Gu, X.; Guise, K.G.; Liu, X.; Fossella, J.; Wang, H.; Posner, M.I. Testing the behavioral interaction and integration of
attentional networks. Brain Cogn. 2009, 70, 209–220. [CrossRef]

Sensors 2021, 21, 5213

30.
31.
32.
33.
34.
35.
36.
37.
38.
39.

40.
41.

42.
43.

44.
45.
46.

15 of 15

Chikara, R.K.; Komarov, O.; Ko, L.-W. Neural signature of event-related N200 and P300 modulation in parietal lobe during
human response inhibition. Int. J. Comput. Biol. Drug Des. 2018, 11, 171–182. [CrossRef]
Polich, J. Updating P300: An integrative theory of P3a and P3b. Clin. Neurophysiol. 2007, 118, 2128–2148. [CrossRef]
Curran, T.; Hills, A.; Patterson, M.B.; Strauss, M.E. Effects of aging on visuospatial attention: An ERP study. Neuropsychologia
2001, 39, 288–301. [CrossRef]
Castle, P.; van Toller, S.; Milligan, G. The effect of odour priming on cortical EEG and visual ERP responses. Int. J. Psychophysiol.
2000, 36, 123–131. [CrossRef]
Chikara, R.K.; Lo, W.-C.; Ko, L.-W. Exploration of brain connectivity during human inhibitory control using inter-trial coherence.
Sensors 2020, 20, 1722. [CrossRef]
Featherstone, C.R.; Morrison, C.M.; Waterman, M.G.; MacGregor, L.J. Semantics, syntax or neither? A case for resolution in theinterpretation of N500 and P600 responses to harmonic incongruities. PLoS ONE 2013, 8, e76600. [CrossRef]
Magosso, E.; de Crescenzio, F.; Ricci, G.; Piastra, S.; Ursino, M. EEG Alpha Power Is Modulated by Attentional Changes during
Cognitive Tasks and Virtual Reality Immersion. Comput. Intell. Neurosci. 2019, 2019, 7051079. [CrossRef]
Misselhorn, J.; Friese, U.; Engel, A.K. Frontal and parietal alpha oscillations reflect attentional modulation of cross-modal
matching. Sci. Rep. 2019, 9, 1–11. [CrossRef] [PubMed]
Bacigalupo, F.; Luck, S.J. Lateralized suppression of alpha-band EEG activity as a mechanism of target processing. J. Neurosci.
2019, 39, 900–917. [CrossRef]
Ishii, R.; Canuet, L.; Ishihara, T.; Aoki, Y.; Ikeda, S.; Hata, M.; Katsimichas, T.; Gunji, A.; Takahashi, H.; Nakahachi, T.; et al. Frontal
midline theta rhythm and gamma power changes during focused attention on mental calculation: An MEG beamformer analysis.
Front. Hum. Neurosci. 2014, 8, 406. [CrossRef]
Seeber, M.; Scherer, R.; Wagner, J.; Solis-Escalante, T.; Müller-Putz, G.R. EEG beta suppression and low gamma modulation are
different elements of human upright walking. Front. Hum. Neurosci. 2014, 8, 485. [CrossRef]
Crone, N.E.; Miglioretti, D.L.; Gordon, B.; Sieracki, J.M.; Wilson, M.T.; Uematsu, S.; Lesser, R.P. Functional mapping of humansensorimotor cortex with electrocorticographic spectral analysis. I. Alpha and beta event-related desynchronization. Brain J.
Neurol. 1998, 121, 2271–2299. [CrossRef]
Miller, K.J.; Leuthardt, E.C.; Schalk, G.; Rao, R.P.N.; Anderson, N.R.; Moran, D.W.; Miller, J.W.; Ojemann, J.G. Spectral changes incortical surface potentials during motor movement. J. Neurosci. 2007, 27, 2424–2432. [CrossRef]
Severens, M.; Nienhuis, B.; Desain, P.; Duysens, J. Feasibility of measuring event related desynchronization with electroencephalography during walking. In Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine
and Biology Society, San Diego, CA, USA, 28 August–1 September 2012; pp. 2764–2767.
Ko, L.-W.; Komarov, O.; Hairston, W.D.; Jung, T.-P.; Lin, C.-T. Sustained attention in real classroom settings: An EEG study. Front.
Hum. Neurosci. 2017, 11, 388. [CrossRef]
Barsalou, L.W. Grounded cognition. Annu. Rev. Psychol. 2008, 59, 617–645. [CrossRef]
Glenberg, A.M. Few believe the world is flat: How embodiment is changing the scientific understanding of cognition. Can. J. Exp.
Psychol. Rev. Can. Psychol. Expérimentale 2015, 69, 165. [CrossRef]