Research Article

Open Access J Neurol Neurosurg

Volume 18 Issue 4 - March 2024
DOI: 10.19080/OAJNN.2024.18.555999

Copyright © All rights are reserved by Moemi Matsuo

Decoding Attentional Task Performance Using
Electroencephalogram Signals

Moemi Matsuo1* and Takashi Higuchi2
1

Faculty of Rehabilitation Sciences, Nishi Kyushu University, 842-8585 Kanzaki, Saga, Japan2

Department of Physical Therapy, Osaka University of Human Sciences, 566-8501 Settsu, Osaka, Japan

Submission: February 28, 2024; Published: March 06, 2024

*Corresponding author: Moemi Matsuo, Faculty of Rehabilitation Sciences, Nishi Kyushu University, 842-8585 Kanzaki, Saga, Japan. Tel.:+818056090223; E-mail: matsuomo@nisikyu-u.ac.jp; ORCID: 0000-0001-7458-1782

Abstract
Background: Electroencephalogram patterns help in evaluating the extent of ischemic brain injury and predicting functional performance.
Aim: To determine a possible correlation between attentional task performance and electroencephalogram waves.

Methods: The cerebral activity of 12 healthy young adults was investigated using an electroencephalogram while they underwent the TrailMaking Test-A and B as attentional tasks.

Results: A significant correlation was observed between a stronger occipital delta power during rest and higher error rates, as well as weakertemporal and central delta power during the Trail-Making Test-B and longer task completion times. Delta waves during both the resting-state
and task conditions correlated with task performance, which might be affected by the induced cerebral lobes.
Conclusion: The default mode network might predict attention deficits. Our findings further our understanding of the correlation between thedefault mode network and attentional task performance.
Keywords: Attentional function; Brain function; Brain injury; Electroencephalography; Neuroimaging
Abbreviation: EEG: Electroencephalogram; DMN: Default Mode Network; TMT: Trail-Making Test

Introduction
Implementation of modular assessments of cognitive
capabilities at the individual level is crucial in the realm of
personalized care and rehabilitation [1]. Rapid transmission of
information across the brain underscores the importance of noninvasive neuroimaging techniques, such as electroencephalogram(EEG) indices. These indices exhibit sensitivity to fluctuationsin the human brain, reflecting spontaneous brain activity withexcellent temporal resolution [2]. Additionally, EEG signals serveas important neuro-electrophysiological indicators of brain
activity [3]. Over the past decade, various new EEG systems
have emerged, enabling the recording of brain activity during
movement [4]. While a single-dimensional measure, such as pupilsize, may be suitable when specific data parameters and goals
are well defined, multidimensional signals offer a more viablechoice. The EEG gathers information on a variety of functions,
including attention, memory, and emotions [5]. Accordingly, theEEG elucidates important effects of brain networks on attentional
function from the perspective of brain connections and provides
Open Access J Neurol Neurosurg 18(5): OAJNN.MS.ID.555999 (2024)

potential physiological biomarkers for predicting attention [6].
Resting-state EEG data can delineate inter-individual variability
at rest and its correlation with attentional capacity and autistic
behavioral patterns [7]. Different EEG patterns reflect various
degrees of ischemic brain injury, with some proving reliablepredictors of functional performance [8].

Attention is a fundamental component of all the cognitiveand perceptual processes [9]. Similar to other networks in thebrain, the human attention system is complex. At any moment,
attention can shift between the external and internal stimuli[10]. The literature highlights three primary mechanisms: global,
local, and self-attention mechanisms [11]. Attention, as a limited
cognitive resource, selectively focuses on discrete aspects of
information while ignoring others; it is typically divided into two
types: active (top-down or endogenous) and passive (bottom-up
or exogenous) [12]. An externally directed attentive state often
corresponds to reduced focus on internally oriented mental
processes [13]. Sufficiently strong attentive states may impair
001

Open Access Journal of Neurology & Neurosurgery
conscious awareness of both the environment and oneself [14].
Previous studies have revealed the functional roles of alpha (α)band oscillations [15], which are the most prevalent rhythms
in EEG recordings and spread through most cortical regions[15]. However, the role of the alpha wave remains debatable[15]. Elevated parietal α power [16] is associated with a shift
from attention to the default mode, which suppresses external
stimulus processing [17]. This increase is usually accompanied byan increase in frontal theta (θ) power [16], reflecting cognitive
control-task execution, memory function, and error processing[17,18]. Greater θ activity is believed to reflect heightened effort
in meeting the increasing cognitive demands of prolonged tasks[16]. Furthermore, heightened power in slow-frequency bands(i.e., θ and α) is associated with lower performance [17]. Whenthe functional state of the brain is suppressed, slow waves withfrequencies below 8 Hz occur, namely, δ waves and θ waves [19].

This discourse between power band-associated cognitive
performance remains unresolved. To develop novel methods forevaluating attentional function, we must further investigate thefundamental brain mechanisms involved in attentional tasks.
Therefore, this study aimed to determine a potential correlation
between attentional task performance and EEG waves. By delving
into the default mode network (DMN), this study not only unveils
insights into attentional deficits but also contributes to ourcomprehension of the DMN’s role.

Methods

Participants
Twelve healthy young adults (seven women and five men;age: 21.3 ± 0.62 years) participated in the present study. All
prospective participants were provided with a comprehensive
explanation of the study’s safety protocols and were assured that
their personal identifying information would remain confidential;
thereafter, they provided written informed consent for study
participation. Additional informed consent was obtained fromall participants whose identifiable information was includedin the study. None of the participants had a history of major
physical disorders, including neurological illnesses, brain injury,
or psychiatric illnesses. This study was approved by the Ethics
Committee of Nishikyushu University (approval no. 22EAB19)and conformed to the principles of the Declaration of Helsinki andits later amendments.

Task

The Trail-Making Test (TMT) comprises two distinct parts:
Part A requires patients to sequentially connect 25 encircled
numbers (through lines) dispersed in a pseudo-random manner
on a sheet; In TMT Part B, participants alternate between numbers
and letters when connecting different items in an ascending order(i.e., 1, A, 2, B, etc.) [20].

002Experimental Setup
Participants were seated in a quiet room on a chair with abackrest and placed their forearms in a relaxed position on a table.
They were instructed to perform the TMT without any additional
movements, such as head movement, and maintain the same
posture throughout the experiment. Additionally, participants
were instructed to remain silent throughout the experiment, relax
without thinking, and fixate their gaze on a cross displayed on apiece of paper in front of them during both task performance andresting periods.

Experimental Protocol

The experimental protocol comprised two tasks, eachpreceded by a 1-min rest period. EEG measurements wererecorded continuously during the experiment using Polymate Pro
MP6100 (Miyuki Giken, Tokyo, Japan). The EEG recorded at thescalp level represents the aggregate currents of the electrical fields
generated by neural activity in cortical neural circuits [21]. Prior
to electrode placement, the skin was prepared with alcohol, andthe electrodes were affixed to an elastic cap using a holder. Basedon the international 10-20 EEG placement system, 19 gold-coatedactive EEG electrodes were placed at specific cortical locations:
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 (rightparietal), O1 (left occipital), O2 (right occipital), T3 (left mid
temporal), T4 (right mid temporal), T5 (left posterior temporal),and T6 (right posterior temporal) (Figure 1).

Data Analysis

The EEG data were sampled at a rate of 1,000 Hz and filtered
within the 1-60 Hz range using a bandpass filter. Data containing
eye blinks or muscle movement artifacts were excluded. Power
spectrum analysis was conducting using the Electro Magnetic
Source Estimation Data Editor (Cortech Solutions, Wilmington,
NC). Six EEG datasets (delta, theta, alpha, beta, low-gamma, andhigh-gamma) were used for each electrode. Furthermore, waves
lying within the 0-4 Hz, 5-8 Hz, 9-13 Hz, 14-30 Hz, 31-50 Hz, and>50 Hz ranges were categorized as delta, theta, alpha, beta, lowgamma, and high-gamma waves, respectively.

Statistical Analysis

The mean power level was calculated for each resting andtask condition. Spearman’s rank correlation was used to examine
the correlation between task performance and EEG power levels
during rest and TMT tasks. Furthermore, IBM SPSS Statistics
version 20.0 (IBM Corp., Armonk, NY, USA) was used for statisticalanalysis. Statistical significance was set at p <0.05.

How to cite this article: Moemi M, Takashi H. Decoding Attentional Task Performance Using Electroencephalogram Signals. Open Access J Neurol
Neurosurg 2024; 18(5): 555999. DOI: 10.19080/OAJNN.2024.18.555999

Open Access Journal of Neurology & Neurosurgery

Figure 1: EEG electrode sets. The EEG electrodes were placed according to the international 10–20 EEG placement method. The Fp1,
Fp2, F3, Fz, F4, F7, F8, C3, Cz, C4, P3, Pz, P4, O1, O2, T3, T4, T5, and T6 were examined. EEG, electroencephalography; 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.

Results
The associations between task performance and EEG power
levels during the TMT-A rest and task conditions are summarizedin Tables 1 and 2, respectively. Notably, no significant correlations
were observed between task performance and EEG power levels

for the TMT-A. The correlations between task performance andEEG power levels in the TMT-B rest and task conditions are
summarized in Tables 3 and 4. Stronger occipital delta power at
rest was significantly correlated with more errors. Additionally,
weaker temporal and central delta power during the task weresignificantly correlated with a longer task time.

Table 1: Correlation of Task Performance and EEG Power Level of the TMT-A Rest.
Task performance

Time

Brain areas (ρ)

Delta

Theta

Alpha

Frontal-0.182-0.091-0.315

Temporal-0.273-0.105-0.175CentralParietal

Occipital
FrontalError

CentralTemporalParietal

Occipital-0.294-0.462-0.3080.453
0.324-0.259-0.105-0.154
0.259
0

0.389

0.065

0.518

0.194

0.389

EEG, electroencephalogram; TMT, Trail-Making Test

0.065

Beta

Low-gamma

High-gamma

0.084

0.182

-0.119

0.266

0.224

0.084

-0.329-0.091-0.364-0.049-0.259-0.13-0.194-0.389-0.259-0.259-0.389-0.322-0.324-0.13-0.32400.175-0.161-0.194
-0.13

0.105
0.077

-0.194-0.389-0.324-0.453

0.028
0.413
0.077

-0.3890-0.453

Table 2: Correlation of Task Performance and EEG Power Level of the TMT-A Task.
Task performance

Time

Brain areas (ρ)

Delta

Theta

Alpha

Beta

Low-gamma

High-gamma

Frontal-0.336-0.308-0.462

0.315

0.364

0.028

Temporal-0.441-0.252-0.168

0.531

0.455

0.301

CentralParietal

Occipital003-0.364-0.413-0.483-0.462-0.091-0.028-0.385-0.434-0.105

0.231
0.28

0.357

0.357
0.385
0.49

0.133
0.371
0.315

How to cite this article: Moemi M, Takashi H. Decoding Attentional Task Performance Using Electroencephalogram Signals. Open Access J Neurol
Neurosurg 2024; 18(5): 555999. DOI: 10.19080/OAJNN.2024.18.555999

Open Access Journal of Neurology & Neurosurgery

Error

Frontal

0.259

0.453

0.324

-0.0650.000-0.194

Temporal

0.259

0.389

0.13

0.000

-0.13

-0.194

CentralParietal

Occipital

0.194

0.324

0.259

0.13

0.389

0.389

0.259

0.518

EEG, electroencephalogram; TMT, Trail-Making Test

0.389

0.000

-0.065-0.0650.000-0.0650-0.0650.000-0.065

Table 3: Correlation of Task Performance and EEG Power Level of the TMT-B Rest.
Task performance

Time

Brain areas (ρ)

Delta

Theta

Alpha

Beta

Low-gamma

High-gamma

Frontal

0.217

0.308

-0.14

0.252

0.294

0.147

Temporal

0.126

0.161

-0.077

0.364

0.336

0.189

CentralParietal

Occipital
FrontalError

CentralTemporalParietal

Occipital

0.021
0.098
0.056
0.389
0.324
0.453
0.453

0.648**P<0.05, Spearman’s rank correlation
EEG, electroencephalogram; TMT, Trail-Making Test

0.077

-0.2170.203-0.1540.196-0.161

0.389

0.324

0.065

0.194

0.194

0.194

0.259

0.389

0.324

0.389

0.266
0.203
0.147
0.324
0.324

0.273
0.091
0.091
0.194

0.147
0.203
0.084
0

0.194

0.259

0.259

0.194

0.389

0.065
0.194

0.13
0

0.065
0.13

Table 4: Correlation of Task Performance and EEG Power Level of the TMT-B Task.
Task performance

Time

Brain areas (ρ)

Delta

Theta

Alpha

Beta

Low-gamma

High-gamma

Frontal-0.455-0.196-0.035

0.399

0.308

-0.084-0.608*-0.273

0.056

0.42

0.294

0.084

Central-0.699*

Parietal-0.531

Temporal
Occipital
FrontalError

CentralTemporalParietal

Occipital*P<0.05, Spearman’s rank correlation

-0.42

0.389
0.194
0.13

0.259
0.259

-0.392-0.014

0.077

0.021

-0.112

0.385

0.518

0.453

0.194

0.028
0.259
0.259
0.389
0.518

0.014

-0.1960.182-0.2940.28

0.063-0.035

0.042

0.324

-0.065

-0.13

0

0.259

0.13

0.13

0.389

0.065

0.13

0.065

0.389

0.324

0.13

0
0

0.259
0.259

EEG, electroencephalogram; TMT, Trail-Making Test

Discussion
Recent studies have suggested that attention operates as arhythmic process, yet the debate continues regarding whether
this rhythmicity is influenced by the phase of the ongoing neuraloscillations. Addressing this issue requires the use of behavioral
tasks that isolate attention from other cognitive functions(perception/decision-making), and the localized monitoringof neural activity with high spatiotemporal resolution acrossthe brain regions associated with the attentional network.22 In
004

the present study, we investigated the relationship between
attentional task performance and EEG frequency using the TMT,
which is a simple behavioral task that isolates attention fromother cognitive functions. The results showed that a stronger
occipital delta power during rest was significantly correlated withmore errors. Moreover, weaker temporal and central delta power
during TMT-B was significantly correlated with a longer task time.
No significant correlations were found between task performanceand EEG power levels for TMT-A.

How to cite this article: Moemi M, Takashi H. Decoding Attentional Task Performance Using Electroencephalogram Signals. Open Access J Neurol
Neurosurg 2024; 18(5): 555999. DOI: 10.19080/OAJNN.2024.18.555999

Open Access Journal of Neurology & Neurosurgery
Notably, the modulation of behavioral outcomes throughEEG frequencies of 3, 6, and 8 Hz (delta, theta, and alpha bands)
throughout the frontal region has been reported in studies
focusing on quantifying the phase that predicts the high andlow attention states [22]. High amplitudes and percentagesof delta waves in the frontal lobe for Fz-F3-F4 demonstrates
strong stimulation of the limbic system, strong emotions, flurry
thoughts, and confusion [23]. Stress, uncertainty, tension, andthe release of adrenaline can contribute to heightened arousal
and impact cognitive state [24]. The most dominant patterns ofchange in patients with attention deficit hyperactivity disorder,
schizophrenia, and obsessive-compulsive disorder are powerincreases across lower frequencies (delta, 1-4 Hz and theta, 4-8 Hz)
and decreases across higher frequencies (alpha, 8-13 Hz; beta, 1330 Hz and gamma, 30-50 Hz) [25]. Impairment of visual attention
is associated with differences in EEG features, network activation,and involvement of the right frontal eye field [26]. In contrast, ourfindings showed that frontal delta power was not correlated withtask performance, while stronger occipital delta power duringrest was significantly correlated with more errors, and weakertemporal and central delta power during TMT-B was significantly
correlated with a longer task time. These results indicate that the
influence of delta waves on task performance may be mediatedby their interaction with the cerebral lobes, highlighting theirrole in task specificity. Moreover, these phenomena affect the task
performance’s specificity. Furthermore, attentional deficits may
be reflected differentially by resting-state and attentional-state
EEG data, implying that the DMN may predict attentional deficits.
Additionally, a correlation exists between different EEG
frequencies and attentional task performance. A higher theta-beta
ratio reflects challenges in attention allocation for a given task
[27]. Gamma band activity plays a vital role in many sensory andcognitive processes. Therefore, the peak frequency in the gamma
range has received considerable attention [28]. Gamma band
activity, combined with frontal midline theta rhythm in the left
and right frontal forehead regions, appears to reflect prefrontal
function in working memory tasks [29]. Furthermore, lateralized
patterns of band phase coupling between the frontal and parietaloccipital regions have been identified during covert visuospatialattention tasks in healthy young adults [30]. While these findings
enrich our understanding of the brain mechanisms underlying
attention, further exploration into additional EEG frequency
considerations and their potential impact on task performance,
particularly lateralized patterns, is essential.

This study had some limitations. It focused solely on healthy
young adults; therefore, it is unclear whether our results canbe generalized to older patients or those with neurologicaldisabilities. Furthermore, the attentional task only utilized the
TMT; therefore, whether brain waves during other attentionaltasks are comparable to those observed during the TMT remains
unclear. Additionally, our study included only a small number005

of participants. To address these limitations, future studies
should include more participants performing tasks undervarious conditions and investigate brainwave activity acrossdiverse attentional tasks. Despite these limitations, these study
findings indicate that stronger occipital delta power during rest
is correlated with errors, and weaker temporal and central delta
power during tasks is linked to a longer task times, offering
insights into the role of brain rhythms in attentional deficits andcontributing to our understanding of cognitive functions andneural connections.

Conclusion

Our study identified significant correlations between EEG
power levels and attentional task performance. Specifically,stronger occipital delta power during rest was significantly
correlated with increased errors, while weaker temporal andcentral delta power during TMT-B was significantly associatedwith a longer task time. Delta waves during both the resting-state
and task conditions were correlated with task performance, which
may also be affected by the induced cerebral lobes. Moreover,
our findings suggest the potential predictive value of the DMN
for attention deficits. Our findings expand the understanding of
the interplay between DMN and attentional task performance,emphasizing the need for further investigations into the intricate
relationships between EEG frequencies and attentional task
performance.

Acknowledgments

The authors extend their gratitude to all the study participants.
This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interests for this article.

Ethics Approval

The study was conducted in accordance with the Declarationof Helsinki and was approved by the Ethics Committee of Nishi
Kyushu University (approval no. 22EAB19) for studies involving
humans.

Patient Consent

Informed consent was obtained from all subjects involved in
the study.

Data Availability Statement

The data that support the findings of this study are available
from the corresponding author upon reasonable request.

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How to cite this article: Moemi M, Takashi H. Decoding Attentional Task Performance Using Electroencephalogram Signals. Open Access J Neurol
Neurosurg 2024; 18(5): 555999. DOI: 10.19080/OAJNN.2024.18.555999

Open Access Journal of Neurology & Neurosurgery
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How to cite this article: Moemi M, Takashi H. Decoding Attentional Task Performance Using Electroencephalogram Signals. Open Access J Neurol
Neurosurg 2024; 18(5): 555999. DOI: 10.19080/OAJNN.2024.18.555999

Open Access Journal of Neurology & Neurosurgery

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DOI: 10.19080/OAJNN.2024.18.555999

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How to cite this article: Moemi M, Takashi H. Decoding Attentional Task Performance Using Electroencephalogram Signals. Open Access J Neurol
Neurosurg 2024; 18(5): 555999. DOI: 10.19080/OAJNN.2024.18.555999