Medicine®

Clinical Trial/Experimental Study

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Assessing attentional task-related
electroencephalogram signal variations by using
mobile electroencephalogram technology
An experimental study
Moemi Matsuo, PhDa,* , Takashi Higuchi, PhDb, Hiroya Miyabara, PhDa, Misako Higashijima, PhDa,
Takeshi Oshikawa, PhDa, Masatoshi Nakamura, PhDa, Yuji Yamaguchi, PhDc, Takuya Higashionna, PhDd

Abstract
Background: A better understanding of the network responses of cortical activities during rest and cognitive tasks is necessary.

Therefore, in this study, we aimed to evaluate cerebral activities during attentional tasks by using mobile electroencephalography,
identifying the types of attentional components and brain waves.
Methods: In this experimental study, we enrolled 12 healthy young adults. The attentional tasks comprised parts A and B of

the Trail-Making Test (TMT). Nineteen electroencephalography electrodes were placed over various brain regions. The Wilcoxon
signed-rank test was used to examine the differences in power levels between the rest and TMT conditions.
Results: During TMT part A, the electroencephalography power level of the delta waves was significantly higher in the rightfrontal, left central, left occipital, left inferior frontal, right mid-temporal, right posterior temporal, and middle parietal areas (P < .05)
than those during the resting state; that of the alpha waves was significantly lower in the left posterior temporal area (P = .006);
and that of the high gamma waves was significantly lower in the left parietal (P = .05) and left occipital (P = .002) areas. During
TMT part B, the electroencephalography power level of the beta waves was significantly higher in the right frontal area (P = .041)
than that during the resting state, and that of the low gamma waves was significantly higher in the left frontal pole, right frontal,
and right inferior frontal areas (P < .05). During the focused attentional task, the power level of the delta waves increased and thatof the alpha waves decreased, and during the alternating attentional task, those of both the beta and gamma waves increased.
The delta waves were related to the whole brain, the alpha and high gamma waves to the left posterior lobe, and the beta andlow gamma waves to both frontal lobes.
Conclusion: These findings contribute to the basic knowledge necessary to develop new attentional assessment methods forclinical situations.
Abbreviations: C3 = left central electrode, C4 = right central electrode, Cz = middle central electrode, F3 = left frontal electrode,F4 = right frontal electrode, F7 = left inferior frontal electrode, F8 = right inferior frontal electrode, Fp1 = left frontal pole electrode,Fp2 = right frontal pole electrode, Fz = middle frontal electrode, O1 = left occipital electrode, O2 = right occipital electrode, P3= left parietal electrode, P4 = right parietal electrode, Pz = middle parietal electrode, T3 = left mid-temporal electrode, T4 = right
mid-temporal electrode, T5 = left posterior temporal electrode, T6 = right posterior temporal electrode, TMT = Trail-Making Test.
Keywords: attention, EEG, electroencephalogram, higher brain function, neuroimaging, neurorehabilitation, neuroscienceas the individual tries to focus on more than one stimulus
at a time.[3] Attention issues may affect the seamless acquisition of new information and consistent performance inlearning and tasks.[4] Attention deficits are linked to a higher
degree of functional impairment and number of falls among

1. Introduction
Attention is the ability to maintain a selective or sustained concentration[1] and support other cognitive abilities.[2] Attention
deficits commonly lead to difficulties in ignoring distractions

The authors have no conflicts of interest to disclose.

Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it ispermissible to download, share, remix, transform, and buildup the work provided
it is properly cited. The work cannot be used commercially without permission
from the journal.

The datasets generated during and/or analyzed during the current study are not publicly
available, but are available from the corresponding author on reasonable request.
aFaculty of Rehabilitation Sciences, Nishi Kyushu University, Kanzaki, Saga,Japan, b Department of Physical Therapy, Osaka University of Human Sciences,
Settsu, Osaka, Japan, c Faculty of Health and Social Welfare Sciences, Nishi
Kyushu University, Kanzaki, Saga, Japan, d Department of Rehabilitation, Facultyof Health Sciences, Tokyo Kasei University, Inariyama, Saitama, Japan.

How to cite this article: Matsuo M, Higuchi T, Miyabara H, Higashijima
M, Oshikawa T, Nakamura M, Yamaguchi Y, Higashionna T. Assessing
attentional task-related electroencephalogram signal variations by using
mobile electroencephalogram technology: An experimental study. Medicine
2023;102:42(e35801).

* Correspondence: Moemi Matsuo, Faculty of Rehabilitation Sciences, Nishi Kyushu
University, Kanzaki, Saga 842-8585, Japan (e-mail: matsuomo@nisikyu-u.ac.jp).

Received: 9 July 2023 / Received in final form: 11 August 2023 / Accepted: 4
October 2023

Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc.
This is an open-access article distributed under the terms of the Creative

http://dx.doi.org/10.1097/MD.0000000000035801

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community-dwelling people who have had a stroke,[5] as well as
attention deficit hyperactivity disorder, which is a neurodevelopmental disorder that affects a person’s sleep, mood, anxiety, andlearning.[6] These attentional deficits can inhibit effective rehabilitation. A randomized controlled trial indicated that early
identification of and rehabilitation for attention deficits shouldbe part of poststroke rehabilitation.[7]The evaluation of intensive cognitive training is usually basedon neuropsychological tests and questionnaires such as Stroop
Test, and Trail-Making Test (TMT).[8] Assessment methods forthe evaluation of outcomes in brain injury rehabilitation need tobe highly sensitive to and provide a detailed analysis of behavioral changes.[9] The effectiveness of therapy can be optimized by
selecting the treatment according to the diagnosis.[10] Learning
processes, thoughts, and various activities are controlled by
cerebral functions. The attentional function should be correctly
evaluated for more effective treatment. However, assessment
results do not always correspond to clinical symptoms. Thus,
more accurate evaluation methods need to be developed to better understand patients’ symptoms.
Electroencephalography is a noninvasive method, commonly used to show changes in cerebral function or brain
activity within a short period of time. It is sensitive, relatively inexpensive, yields high temporal resolution, allows
portable monitoring for clinical assessment and otherapplications, and provides a variety of useful data from ashort examination.[10] This electronic, neurophysiological,
experimental method can be used to investigate the brain’sfunctional status in real time while the patient is focusing
on assignments. It is applicable to various patients and canbe used to objectively analyze brain functions. As electroencephalography enables observation of spatiotemporalchanges in the brain, it is very useful for real-time measurement of cognitive load.[11]Cortical network activities, including the default mode andattention networks, change as the functional brain state changes
from different task-positive states to the resting state.[12] Oh
and Song emphasized the importance of measuring brainwaves
in many parts of the brain of patients to tailor treatments toimprove their attentional functions.[13] However, further investigation is required to better understand the network responses ofcortical activities during rest and cognitive tasks.[14] Therefore,
in this study, we aimed to evaluate cerebral activity during attentional tasks by using mobile electroencephalography, identifying
the types of attentional components and brain waves.

2 parts: part A requires patients to sequentially connect 25
encircled numbers pseudo-randomly distributed on a sheetof paper, while part B requires patients to alternate between
numbers and letters (i.e., 1, A, 2, B, etc).[18] TMT-A is typicallyconceived as a measure of visual search and processing speed,
whereas TMT-B is more generally assumed to measure mental
flexibility and executive function.[17] For TMT-A and TMT-B,
the “average” and “deficient” scores for adults aged < 54 years
are categorized as follows: TMT-A, 29 seconds (average), >79
seconds (deficit); TMT-B, 75 seconds (average), >273 seconds(deficit).[19] Patients were asked to perform the tasks as fast as
they could, and not to release the tip of a pencil from a piece
paper of the task. The researchers announced when to begin
the task and when to rest. The task end time was the moment
patients completed the task.
2.2.2. Experimental setup. Participants sat on a chair witha backrest in a silent room. They placed their forearms in arelaxed position on the table and were instructed to performthe tasks in the TMT without making other movements, suchas head movements, to maintain the same posture, and not tospeak throughout the experiment. Moreover, they were asked tomaintain the same posture, relax without thinking, and look at
the cross on the paper in front of them during rest.
2.2.3.
Electroencephalography. Electroencephalography
data were obtained during the task and resting conditions.
The skin was sterilized with alcohol, and the electrodes were
mounted on an elastic cap with electrode holders. Nineteen
gold-coated active electroencephalography electrodes wereplaced on Fp1 (left frontal pole), Fp2 (right frontal pole), F3(left frontal), Fz (middle frontal), F4 (right frontal), F7 (leftinferior frontal), F8 (right inferior frontal), C3 (left central),Cz (middle central), C4 (right central), P3 (left parietal), Pz(middle parietal), P4 (right parietal), O1 (left occipital),
O2 (right occipital), T3 (left mid-temporal), T4 (right midtemporal), T5 (left posterior temporal), and T6 (rightposterior temporal), in accordance with the international 10
to 20 electroencephalography placement system (Fig. 1). Brainelectrical activity was measured continuously during the tasks
and in the resting state (Fig. 2) by using the Polymate Pro
MP6100 (Miyuki Giken, Tokyo, Japan) biosignal recording
device. The task order was counterbalanced.
2.3. Data analysis
Electroencephalography data were sampled at 1000 Hz andfiltered from 1 to 60 Hz using a bandpass filter. Data containing eye blink or muscle movement artifacts were excluded
using a filter that automatically detects specific eye movements
using the EMSE Data Editor suite (EMSE; Cortech Solutions,Inc., Wilmington, NC). Power spectrum analysis was also
performed using EMSE. Six electroencephalography datasets(delta, theta, alpha, beta, low-gamma, and high-gamma) wererecorded for each electrode. The categorization was as follows: 0 to 4 Hz, delta; 5 to 8 Hz, theta; 9 to 13 Hz, alpha; 14
to 30 Hz, beta; 31 to 50 Hz, low gamma; and >50 Hz, highgamma waves. The power calculation window time was set as
2 seconds.

2. Methods
2.1. Participants
In this experimental study, we enrolled 12 healthy young
adults (7 women and 5 men; mean ± standard deviation
age: 21.3 ± 0.62 years). The participants were informed of
the safety regulations of this study and were assured thatnone of their identifying information would be disclosed.
All participants provided written informed consent for study
participation and for publication of the results. None ofthe participants had a history of major physical disorders,
including neurological illnesses, brain injury, or psychiatricillnesses. This study was approved by the Ethics Committeeof Nishi Kyushu University (approval no.: 22EAB19) andconformed to the principles of the Declaration of Helsinki[15]and its later amendments.

2.4. Statistical analysis
The mean power level was calculated for each rest and task
condition. The Wilcoxon signed-rank test was used to examine
the differences in power levels between the rest and TMT conditions. IBM SPSS Statistics for Windows (version 20.0; IBM
Corp., Armonk, NY) was used for the analyses. Differenceswere considered statistically significant at P < .05.

2.2. Experimental protocol
2.2.1. Task. The TMT, one of the most widely used cognitive
tasks for the measurement of attention and executive function,
was used as an attentional task.[16,17] This test typically comprises
2

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Figure 1. Electroencephalography electrode placement. The electroencephalography electrodes were placed according to the international 10 to 20
electroencephalography placement method. Fp1, Fp2, F3, Fz, F4, F7, F8,
C3, Cz, C4, P3, Pz, P4, O1, O2, T3, T4, T5, and T6 were used. Fp1 = left
frontal pole; Fp2 = right frontal pole; F3 = left frontal; Fz = middle frontal; F4= right frontal; F7 = left inferior frontal; F8 = right inferior frontal; C3 = left
central; Cz = middle central; C4 = right central; P3 = left parietal; Pz = middle
parietal; P4 = right parietal; O1 = left occipital; O2 = right occipital; T3 = left
mid-temporal; T4 = right mid-temporal; T5 = left posterior temporal; T6 = rightposterior temporal.

Figure 2. Experimental protocol. The experiment was performed with 2 tasks,
and a 1-minute rest was allowed before each task. Electroencephalographymeasurements were acquired continuously during the tasks and the restingstate. EEG = electroencephalography; TMT = Trail-Making Test.

electroencephalography power levels of the beta waves and lowgamma waves were significantly higher than those in the rest
state in the frontal lobe during TMT-B. Electroencephalography
rhythms can be categorized according to their frequency ranges.
Traditionally, 6 major rhythms have been used: delta (0–4 Hz),theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), low-gamma
waves (30–50 Hz), and high-gamma waves (> 50 Hz).[20] Thesensitivity of electroencephalography to changes in mental
activity has been recognized since Berger[21] reported a decreasein the amplitude of the dominant (alpha) electroencephalography rhythm during mental arithmetic.
Previous studies have addressed the relationship between
attention and brainwaves. Strong negative correlations ofalpha power with subjective sleepiness at all scalp locations
suggest a negative association between sleepiness and general
cortical activation; and positive correlations of theta powerwith subjective sleepiness with a focus on frontal locations
suggest additional location-specific associations between
sleepiness and cortical activation.[22] Under the eyes-open
condition, strong default mode network activity is associatedwith reduced visual cortical excitability, which serves to block
external visual input from interfering with introspective mental
processing mediated by the default mode network, while weak
default mode network activity is associated with increased
visual cortical excitability, which helps to facilitate stimulus
processing, and under the eyes-closed condition, the lack of
external visual input renders such a gating mechanism unnecessary.[23] It contributes to perceptual facilitation related to feature-based visual attention.[24–27] Thus, during TMT-A, which
is a focused attentional task, the power level of delta waves
increased and that of alpha waves decreased, suggesting thatthe task unconsciously switched the participants’ brains fromthe default mode to the attentional mode. Conversely, the beta
wave is prominent when a healthy person is excited or concentrates on a certain task, is usually displayed in the frontal andparietal regions,[13] and is associated with increased vigilance
and attention.[9,23,24] The gamma waves are related to focused
attention.[28] Therefore, during TMT-B, which is an alternating

3. Results
The participants’ grand average waveforms are presented in
Figure 3, and the integrated topographic map is presented
in Figure 4. Power level differences between the restingstate and attentional tasks were observed. A comparison of
the electroencephalography power levels at rest and during
TMT-A is presented in Figure 5. During TMT-A, the electroencephalography power level of the delta waves was significantly higher in F4 (P = .041), C3 (P = .028), O1 (P = .034),
F7 (P = .034), T4 (P = .041), T6 (P = .041), and Pz (P = .05)
than those during the resting state, while that of the alphawaves was significantly lower in T5 (P = .006) and that of
the high gamma waves was significantly lower in P3 (P = .05)
and O1 (P = .002). A comparison of the electroencephalography power levels between the resting and TMT-B conditions
is presented in Figure 6. During TMT-B, the electroencephalography power level of the beta waves was significantly
higher in F4 (P = .041) than that during the resting state, andthat of the low gamma waves was significantly higher in Fp1(P = .034), F4 (P = .028), and F8 (P = .034). Moreover, allof the participants fully completed the tasks, so none of thedata were excluded. TMT-A results indicated a task time of
65.18 ± 11.06 seconds and error of 0.17 ± 0.39 times. TMT-B
results indicated a task time of 61.36 ± 6.18 seconds and errorof 0.17 ± 0.39 times.

4. Discussion
In this study, we compared the power level of brain waves during
the resting state to that during the performance of TMT-A andTMT-B by using mobile electroencephalography to identify
attentional components. Compared to the resting state, the electroencephalography power level of the delta waves was significantly higher in the whole brain; that of the alpha waves was
significantly lower in T5; and that of the high gamma waves was
significantly lower in P3 and O1 during TMT-A. In contrast, the3Matsuo et al. • Medicine (2023) 102:42Medicine

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Figure 3. Electroencephalography power spectrum waveforms. The black lines indicate the electroencephalography power levels of the task conditions, andthe blue line indicates the electroencephalography power level of the rest conditions. The x-axis indicates frequencies, and the y-axis indicates power levels. (A)Comparison of rest and Trail-Making Test part A. (B) Comparison of rest and Trail-Making Test part B. Fp1 = left frontal pole; Fp2 = right frontal pole; F3 = left
frontal; Fz = middle frontal; F4 = right frontal; F7 = left inferior frontal; F8 = right inferior frontal; C3 = left central; Cz = middle central; C4 = right central; P3 =left parietal; Pz = middle parietal; P4 = right parietal; O1 = left occipital; O2 = right occipital; T3 = left mid-temporal; T4 = right mid-temporal; T5 = left posterior
temporal; T6 = right posterior temporal.

Figure 4. Integrated electroencephalography topographic map. Hotter colored spots indicate higher brainwave power levels and cooler colored spots indicate
lower brainwave power levels. (A) During rest before Trail-Making Test part A. (B) During Trail-Making Test part A. (C) During rest before Trail-Making Test partB. (D) During Trail-Making Test part B.

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Figure 5. Comparison of the electroencephalography power levels between the resting state and Trail-Making Test part A. The electroencephalography power
levels were compared using the Wilcoxon signed-rank test. The figure displays only those results with significant differences between the resting state and TrailMaking Test part A. The white bar represents the power level during the resting state, whereas the black bar represents the power level during Trail-Making Test
part A. (A) Delta power level. (B) Alpha power level. (C) High gamma power level. *P < .05, **P < .01. TMT = Trail-Making Test.

Figure 6. Comparison of the electroencephalography power levels between the resting state and Trail-Making Test part B. The electroencephalography power
levels were compared using the Wilcoxon signed-rank test. The figure displays only those results with significant differences between the resting state and TrailMaking Test part B. The white bar represents the power level during the resting state, whereas the black bar represents the power level during Trail-Making Test
part B. (A) Beta power level. (B) Low gamma power level. *P < .05. TMT = Trail-Making Test.

to both frontal lobes. The occurrences of alpha and beta aresimilar to other reports, while occurrences of delta and gamma
suggest these frequency power bands also may be key to predicting attentional functions. Additionally, previous evidence
indicates a relationship between visual attention accuracy andan increase in the EEG beta frequency power in the occipital region of the brain.[31] Another previous study suggested
that attentional tasks are strongly related to right brain wave
rhythms, with some crossover to the left hemisphere, whereas
alertness was more strongly linked to brain wave rhythms on
the left side.[32] In this study, the delta waves were related to thewhole brain, the alpha and high gamma waves to the left posterior lobe, and the beta and low gamma waves to both frontal
lobes. However, the neurophysiological characteristics of thebrain are not clear.[29] Therefore, further research is required toaddress this issue.

attentional task, the power levels of beta and gamma waves are
increased, suggesting that the participants concentrated on thetask, which excited their brains.
In terms of task performance and brain areas, previous studies investigated the behavioral benefits of participants regarding visuospatial selective attention.[29] Neurophysiologicalstudies suggest a correlation between task performancesand EEG signals. Parieto-occipital alpha (7–15 Hz; thought
to modulate attentional focus) and frontal beta (13–30 Hz;
associated with maintenance of the current sensorimotor stateand predictive coding) oscillations covary with trial-wise percent-correct scores; importantly, alpha and beta power provide
significant independent contributions to predicting single-trial
behavioral outcomes.[30] In this study, the delta waves wererelated to the whole brain, the alpha and high gamma waves
to the left posterior lobe, and the beta and low gamma waves
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This study had several limitations. First, all participants were
healthy young adults. Therefore, the generalizability of ourresults to older adults or patients with neurological disabilities
is unclear. Second, the attentional task was limited to the TMT;
thus, the results may not be generalizable to other attentionaltasks. Third, our study included only a small number of participants. Future studies should include more participants with various conditions, such as those with dementia, and investigate thebrainwaves during various attentional tasks. Moreover, moredetailed statistical analysis is also needed to validate findings
like ours, and for classifying the brain’s attentional functions
with larger samples in the future.
In conclusion, the power levels of the brain waves differed
between the resting state and the performance of attentionaltasks. During the focused attentional task, the delta waves
increased and the alpha waves decreased. During the alternating attentional task, the beta and gamma waves both increased.
The delta wave was related to areas throughout the brain, thealpha and high gamma waves to the left posterior lobe, and thebeta and low gamma waves to both frontal lobes. This study
contributes to the basic knowledge necessary to develop new
attentional assessment methods for clinical situations.

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Acknowledgments
The authors extend their gratitude to all the study participants.

Author contributions
Conceptualization: Moemi Matsuo.
Data curation: Moemi Matsuo.
Formal analysis: Moemi Matsuo, Takashi Higuchi.
Funding acquisition: Moemi Matsuo.
Investigation: Moemi Matsuo.
Methodology: Moemi Matsuo.
Project administration: Moemi Matsuo.
Resources: Moemi Matsuo.
Software: Moemi Matsuo.
Supervision: Moemi Matsuo.
Validation: Moemi Matsuo.
Visualization: Moemi Matsuo.
Writing – original draft: Moemi Matsuo.
Writing – review & editing: Moemi Matsuo, Hiroya Miyabara,
Misako Higashijima, Takeshi Oshikawa, Masatoshi
Nakamura, Yuji Yamaguchi, Takuya Higashionna.

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