Open Journal of Therapy and Rehabilitation, 2024, 12, 1-9
https://www.scirp.org/journal/ojtr
ISSN Online: 2332-1830
ISSN Print: 2332-1822

Enhancement of Visual Attention by Color
Revealed Using Electroencephalography
Moemi Matsuo1*, Takashi Higuchi2, Takuya Ishibashi1, Ayano Egashira1, Toranosuke Abe1,
Hiroya Miyabara1
1
2

Faculty of Rehabilitation Sciences, Nishikyushu University, Kanzaki, Japan
Department of Physical Therapy, Osaka University of Human Sciences, Settsu, Japan

How to cite this paper: Matsuo, M., Higuchi, T., Ishibashi, T., Egashira, A., Abe,T. and Miyabara, H. (2024) Enhancementof Visual Attention by Color Revealed Using
Electroencephalography. Open Journal of
Therapy and Rehabilitation, 12, 1-9.
https://doi.org/10.4236/ojtr.2024.121001
Received: November 26, 2023
Accepted: January 1, 2024
Published: January 4, 2024
Copyright © 2024 by author(s) and
Scientific Research Publishing Inc.
This work is licensed under the Creative
Commons Attribution International
License (CC BY 4.0).
http://creativecommons.org/licenses/by/4.0/

Open Access

DOI: 10.4236/ojtr.2024.121001

Abstract
Attention constitutes a fundamental psychological feature guiding our mental
effort toward specific objects, concurrent with processes such as memory,
reasoning, and imagination. Visual attention, crucial for selecting surrounding information, often decreases in older adults and patients with cerebrovascular disorders. Effective methods to enhance attention are scarce. Here,
we investigated whether color information influences visual attention andbrain activity during task performance, utilizing EEG. We examined 13
healthy young adults (seven women and six men; mean age: 21.2 ± 0.58 years)
using 19-electrode electroencephalograms to assess the impact of color information on visual attention. The Clinical Assessment for Attention cancellation test was conducted under the black, red, and blue color conditions.
Wilcoxon’s signed-rank test was used to assess differences in task performance (task time and error) between conditions. Spearman’s rank correlation
was utilized to examine the correlation in power levels between task performance and color conditions. Significant variations in total task errors wereobserved among color conditions. The black condition exhibited the highest
error frequency (0.7 ± 0.9 times), followed by the red condition (0.5 ± 0.8
times), with the lowest error frequency occurring in the blue (0.2 ± 0.4 times)
condition (black vs. red: P = 0.03; black vs. blue: P = 0.00; red vs. blue: P =0.032). No time difference was observed. The black condition showed negative delta and high-gamma correlations in the central electrodes. The red
condition revealed positive alpha and low-gamma correlations in the frontal
and occipital areas. Although no correlations were observed in the blue condition, it enhanced attentional performance. Positive alpha and low-gamma
waves might be crucial for spotting attentional errors in key areas. Our findings provide insights into the effects of color information on visual attention
and potential neural correlates associated with attentional processes. In con-Jan. 4, 2024

1

Open Journal of Therapy and Rehabilitation

M. Matsuo et al.

clusion, our study implies a connection between color information and attentional task performance, with blue font associated with the most accurate
performance.

Keywords
Attention, Higher Brain Function, Electroencephalography, Neuroimaging,
Rehabilitation

1. Introduction
Attention is a fundamental psychological feature that directs our mental effort
toward specific objects, accompanying processes such as memory, reasoning,
and imagination [1]. There are five attentional components: focused, sustained,
selective, alternating, and divided [2]. Attention reliably modulates neural activity in the primary and secondary cortices, affecting the mean neuronal firing
rate, variability, and correlation across neurons [3] [4].
Electroencephalogram (EEG) signals can measure attention levels [1]. An
EEG records nerve activity in the brain through electrodes placed on the scalp,
and changes in activity correlate to changes in mental state, emotion, and cognitive activity [5] [6]. Physiological measures using EEGs are considered accurate[7] and reliable [8]. However, few studies have assessed multilevel attention classification based on EEG, and feature screening has rarely been considered [1].
The EEG can be divided into five rhythms (frequency bands: δ, θ, α, β, and γ),with different rhythms having specific characteristics [9].
Visual attention is a cognitive process that mediates the selection of important
information from the environment, governed by bottom-up and top-down attentional biases [10]. The nervous system utilizes this mechanism to highlightspecific locations, objects, or features within the visual field, which can beaccomplished through eye movements that bring the object onto the fovea(overt attention) or by enhancing visual information processing in neurons,
representing more peripheral regions of the visual field (covert attention) [11].
Typically, reduced attention is observed in older adults and patients with cerebrovascular disorders such as stroke and dementia. Although prior studies
have indicated the potential influence of color on reaction time and memory
during visual attention tasks [12], its effect on task accuracy and its mechanism
remain unclear. Therefore, in the current study, we aimed to investigate whether
color information affects visual attention and brain activity during task performance using EEG.

2. Materials and Methods
2.1. ParticipantsThis study included 13 healthy young adults (seven women and six men; age:
DOI: 10.4236/ojtr.2024.121001

2

Open Journal of Therapy and Rehabilitation

M. Matsuo et al.

21.2 ± 0.58 years). The inclusion criteria were the absence of a history of major
physical disorders, including neurological illnesses, brain injuries, or psychiatricillnesses, while the exclusion criteria encompassed a history of any of these conditions. All prospective participants were informed of the safety regulations of
this study and assured that their identifying information would remain confidential. Written informed consent was obtained from all participants before participation. This study was approved by the Ethics Committee of Nishikyushu
University (approval no. 22BFF20) and conformed to the principles of the Declaration of Helsinki.

2.2. Task
The study employed the cancellation test of the Clinical Assessment for Attention (CAT) by the Japan Society for Higher Brain Dysfunction. Participants were
instructed to eliminate the target number from the interfering stimulus (othernumbers) as quickly as possible without overlooking it. The task was performed
under three color conditions: black, red, and blue font color.

2.3. Experimental Setup
Participants were seated on a chair with a backrest in a quiet room. They rested
their forearms on a table in a relaxed position and were instructed to performthe CAT without additional movements (e.g., head movements). The participants were asked to remain silent during the experimental period. They wererequired to maintain a consistent posture, relax and clear their heads, and lookat the cross-fixation point on a piece of paper in front of them during rest periods.

2.4. Experimental Protocol
The experiment consisted of a task with three color conditions, with a one-minuterest period between each condition. The task was performed until the participants completed it, and the order of the tasks was counterbalanced. EEG measurements were performed continuously during the experiment using Polymate
Pro MP6100 (Miyuki Giken, Tokyo, Japan). The skin was prepared with alcohol,
and the electrodes were mounted on an elastic cap using a holder. Nineteen
gold-coated active EEG electrodes were placed at 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), and T6 (right posterior temporal),in accordance with the international 10 - 20 EEG placement system (Figure 1).

2.5. Data Analysis
EEG data were sampled at 1000 Hz and filtered from 1 - 60 Hz using a bandpass
DOI: 10.4236/ojtr.2024.121001

3

Open Journal of Therapy and Rehabilitation

M. Matsuo et al.

Figure 1. Electroencephalogram (EEG) electrode placement. The EEG electrodes wereplaced in accordance with the international 10 - 20 EEG placement method. Fp1, Fp2, F3,
Fz, F4, F7, F8, C3, Cz, C4, P3, Pz, P4, O1, O2, T3, T4, T5, and T6 were examined. 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;
and T6, right posterior temporal.

filter with a digital infinite impulse response filter (low end, 1 Hz; high end, 60
Hz). Data containing eye blinks or muscle movement artifacts were excluded.
Power spectrum analysis was performed using the Electro Magnetic Source Estimation Data Editor (Cortech Solutions Inc., Wilmington, NC). Six EEG datasets (delta, theta, alpha, beta, low-gamma, and high-gamma) were calculated foreach electrode. Specifically, 0 - 4 Hz was categorized as delta, 5 - 8 Hz as theta, 9- 13 Hz as alpha, 14 - 30 Hz as beta, 31 - 50 Hz as low-gamma, and >50 Hz as
high-gamma waves.

2.6. Statistical Analysis
The data were non-parametric, and the mean power level for each resting andcolor condition was calculated. Wilcoxon’s signed-rank test was used to assess
differences in task performance (task time and error) between color conditions.
Spearman’s rank correlation was used to examine the correlation in power levels
between task performance and color conditions. Data are expressed as the mean
± standard deviation. Statistical analyses were performed using SPSS for Windows (version 20.0; IBM, Armonk, NY). Statistical significance was set at P <0.05.

3. Results
Figure 2 shows the integrated topographic map. The total task error differed
DOI: 10.4236/ojtr.2024.121001

4Open Journal of Therapy and Rehabilitation

M. Matsuo et al.

significantly between color conditions, with the highest number of errors occurring in the black (0.7 ± 0.9 times), followed by the red (0.5 ± 0.8 times), and blue
(0.2 ± 0.4 times) conditions (black vs. red: P = 0.03; black vs. blue: P = 0.00; redvs. blue: P = 0.032).
The fastest task time was observed in the blue condition (71.18 ± 12.24 s), followed by the red (71.99 ± 13.30 s), and then the black conditions (76.92 ± 21.42s); however, no significant difference in time was observed (Figure 3).
Table 1 summarizes the correlations between EEG power levels and task errors. Under the black condition, significant negative correlations were observed
between the delta and high-gamma waves in the central region. Conversely, significant positive correlations were observed in alpha and low-gamma waves in
the frontal and occipital areas in the red condition. No significant correlations
were detected under the blue condition.

Figure 2. Integrated electroencephalogram topographic map. Red and blue areas spots
indicate higher and lower frequencies, respectively. (a) Black condition; (b) Red condition; (c) Blue condition.

Figure 3. Comparison of task performance between color conditions. The performance of each task
was compared using the Wilcoxon signed-rank test. (a) Cancellation test of the Clinical Assessment for
Attention (CAT) time; (b) CAT error. *P < 0.05, **P < 0.01.
DOI: 10.4236/ojtr.2024.121001

5Open Journal of Therapy and Rehabilitation

M. Matsuo et al.
Table 1. Correlation of electroencephalography power levels and task error.
Task

Black

Red

Blue

Cerebralregions
(ρ)

Delta

Theta

Alpha

Beta

Frontal−0.137 −0.194

0.373

0.115

0.088

−0.328Central−0.568* −0.446

0.027−0.231−0.161−0.571*

Temporal−0.149 −0.194 −0.009 −0.082−0.052−0.222

Parietal−0.179 −0.109 −0.058 −0.143−0.018−0.471

Occipital−0.0120.055−0.109

0.185

0.018

−0.255

Frontal

0.271

0.365

0.717**

0.365

0.609*−0.068Central

0.203

0.189

0.487

0.447

0.406

−0.068

Temporal

0.217

0.217

0.311

0.392

0.365

0.041

Parietal

0.244

0.952

0.474

0.271

0.541

−0.068

Occipital

0.338

0.311

0.623*

0.217

0.46

0.041

Frontal

0.342

0.488

0.244

0.488

0.244

0.342

Central

0.146

0.195

0.000

0.439

0.098

0.293

Temporal

0.39

0.244

0.244

0.439

0.293

0.195

Parietal

0.098

0.146

−0.049

0.39

0.098

0.098

Occipital

0.195−0.098

0.049

0.293

0.195

−0.049Low-gamma High-gamma**P < 0.01, *P < 0.05 (Spearman’s rank correlation).

4. Discussion
In this study, we investigated the impact of color on attentional tasks and cerebral activation using EEG. We hypothesized that color manipulation would influence EEG frequency, correlating it with task performance. Our findings revealed that most task errors were associated with black font, followed by red, andthen blue font. We noted significant negative delta and high-gamma correlationsin the central region in the black condition. Significant positive alpha andlow-gamma wave correlations were observed in the frontal and occipital regionsin the red condition. However, no significant correlation was observed in theblue-font condition. The negative delta and high-gamma correlations in theblack condition may imply a positive correlation in alpha-wave power levels(Table 1). These results suggest that positive alpha and low-gamma waves could
be key indicators of attentional errors or deficits.
Previous studies have demonstrated the positive effects of blue light on attention and consciousness [13] [14] [15]. For instance, exposure to blue-enriched
white light (17,000 K) can reportedly improve working memory and sustained
attention [16]. The performance results in tasks requiring sustained attention
indicated that exposure to blue-enriched white light did not affect action errors.
Nevertheless, compared with baseline conditions, such exposure could substanDOI: 10.4236/ojtr.2024.121001

6

Open Journal of Therapy and Rehabilitation

M. Matsuo et al.

tially reduce the number of deletions and response errors. Moreover, there was aconsiderable difference between the mean removal error at 17,000 K underblue-enriched white light and baseline conditions. The effects of blue-enriched
white light on cognitive function have also been reported in other studies [15][17] [18] [19]. Our findings suggest that both blue light and blue-colored fonts
can positively impact attentional functions.
Illuminance is an important factor that can indicate the quality of lighting
conditions. Yang et al. [20] reported that illuminance can substantially impact
attention and alertness and that higher illuminance results in higher levels ofalertness and attentiveness. Additionally, exposure to high levels of illuminanceat the eye level can induce subjective alertness and vitality, increase physiologicalarousal, and improve performance in a sustained attention task even in the absence of sleep and with light deprivation [21]. Generally, high illuminance levels
have been associated with increased alertness and concentration, highlighting
their importance in enhancing attention [13].
Frequency bands related to attention mainly include theta, alpha, and beta
waves. Theta waves typically occur during relaxation or fatigue and are primarily
distributed in the central area of the brain. In the awake state, theta waves wereassociated with attentional alertness. Alpha waves are predominant in a calm
state and are mainly detected in the occipital and parietal lobes [9]. Finally, beta
waves are more prominent during excitement and are mainly distributed in thefrontal and central areas [1]. A relationship between visual attention accuracy
and increased EEG beta frequency power in the occipital region has been reported [22]. Black is a common color encountered in daily life, and its ability todraw attention might be limited compared to colors such as blue and red. Unlike
blue and red, black did not substantially impact attention or make it difficult toconcentrate on the tasks in this study. Therefore, this may have led to a relaxedstate rather than a state of concentration or tension during the task. Furthermore, a positive correlation between alpha waves and black color was observed
during the task, suggesting that the black color condition caused the highest
number of errors.
This study had some limitations. First, all participants were healthy young
adults; therefore, whether our results can be generalized to older participants or
those with neurological disabilities is unclear. Second, the attentional task was
limited to CAT; thus, whether brain waves during other attentional tasks arecomparable with those observed during CAT is unclear. Third, this study included only a small number of participants. Thus, future studies should include
more participants under various conditions and assess brain waves during different attentional tasks.

5. Conclusion
Our study suggests an association between color information and attentional
task performance. The use of blue font was linked to the most accurate attentional performance. Notably, positive alpha and low-gamma waves detected in
DOI: 10.4236/ojtr.2024.121001

7

Open Journal of Therapy and Rehabilitation

M. Matsuo et al.

the frontal, central, and occipital brain areas may serve as important indicatorsfor identifying attentional errors or deficits. Conversely, as observed in the frontal and central regions, beta waves appeared to be associated with heightened excitement. Understanding the neural correlates of attention may have practical
implications for optimizing attentional states and cognitive performance in various settings. Further research with more diverse participant populations and abroader range of attentional tasks is essential to confirm and extend our findings.

Acknowledgements
We would like to thank all of the participants for their participation in thisstudy.

Conflicts of Interest
There are no conflicts of interest to declare.

References

DOI: 10.4236/ojtr.2024.121001[1]

Xu, X., Nie, X., Zhang, J. and Xu, T. (2023) Multi-Level Attention Recognition of
EEG Based on Feature Selection. International Journal of Environmental Researchand Public Health, 20, Article 3487. https://doi.org/10.3390/ijerph20043487[2]

Sohlberg, M. and Mateer, C.A. (2001) Cognitive Rehabilitation: An Integrative
Neuropsychological Approach. Guilford Press, New York.

[3]

Herrmann, C.S. and Knight, R.T. (2001) Mechanisms of Human Attention: EventRelated Potentials and Oscillations. Neuroscience & Biobehavioral Reviews, 25,465-476. https://doi.org/10.1016/S0149-7634(01)00027-6[4]

Katsuki, F. and Constantinidis, C. (2014) Bottom-Up and Top-Down Attention:
Different Processes and Overlapping Neural Systems. Neuroscientist, 20, 509-521.
https://doi.org/10.1177/1073858413514136[5]

Chakravarthi, B., Ng, S.C., Ezilarasan, M.R. and Leung, M.F. (2022) EEG-Based
Emotion Recognition Using Hybrid CNN and LSTM Classification. Frontiers in
Computational Neuroscience, 16, Article 1019776.
https://doi.org/10.3389/fncom.2022.1019776[6]

Lin, F.-R. and Kao, C.-M. (2018) Mental Effort Detection Using EEG Data in
E-Learning Contexts. Computers & Education, 122, 63-79.
https://doi.org/10.1016/j.compedu.2018.03.020[7]Ancillon, L., Elgendi, M. and Menon, C. (2022) Machine Learning for Anxiety Detection Using Biosignals: A Review. Diagnostics, 12, Article 1794.
https://doi.org/10.3390/diagnostics12081794[8]

Lal, S.K. and Craig, A. (2001) A Critical Review of the Psychophysiology of Driver
Fatigue. Biological Psychology, 55, 173-194.
https://doi.org/10.1016/S0301-0511(00)00085-5[9]

Clayton, M.S., Yeung, N. and Cohen Kadosh, R. (2015) The Roles of Cortical Oscillations in Sustained Attention. Trends in Cognitive Sciences, 19, 188-195.
https://doi.org/10.1016/j.tics.2015.02.004[10]

Lockhofen, D.E.L. and Mulert, C. (2021) Neurochemistry of Visual Attention. Frontiers in Neuroscience, 15, Article 643597. https://doi.org/10.3389/fnins.2021.643597
8Open Journal of Therapy and Rehabilitation

M. Matsuo et al.
[11]

Bisley, J.W. (2011) The Neural Basis of Visual Attention. Journal of Physiology, 589,
49-57. https://doi.org/10.1113/jphysiol.2010.192666[12]

Bansal, N., Prakash, N.R. and Randhawa, J.S. (2017) Effects of Blue Light on Cognitive Performance. International Research Journal of Engineering and Technology, 4,2434-2442.

[13]

Golmohammadi, R., Yousefi, H., Safarpour Khotbesara, N., Nasrolahi, A. and Kurd,
N. (2021) Effects of Light on Attention and Reaction Time: A Systematic Review.
Journal of Research in Health Sciences, 21, e00529.
https://doi.org/10.34172/jrhs.2021.66[14]

Phipps-Nelson, J., Redman, J.R., Schlangen, L.J. and Rajaratnam, S.M.W. (2009)
Blue Light Exposure Reduces Objective Measures of Sleepiness during Prolonged
Nighttime Performance Testing. Chronobiology International, 26, 891-912.
https://doi.org/10.1080/07420520903044364[15]

Viola, A.U., James, L.M., Schlangen, L.J. and Dijk, D.J. (2008) Blue-Enriched WhiteLight in the Workplace Improves Self-Reported Alertness, Performance and Sleep
Quality. Scandinavian Journal of Work, Environment & Health, 34, 297-306.
https://doi.org/10.5271/sjweh.1268[16]

Motamedzadeh, M., Golmohammadi, R., Kazemi, R. and Heidarimoghadam, R.
(2017) The Effect of Blue-Enriched White Light on Cognitive Performances andSleepiness of Night-Shift Workers: A Field Study. Physiology & Behavior, 177,
208-214. https://doi.org/10.1016/j.physbeh.2017.05.008[17]Chellappa, S.L., Steiner, R., Blattner, P., Oelhafen, P., Götz, T. and Cajochen, C.
(2011) Non-Visual Effects of Light on Melatonin, Alertness and Cognitive Performance: Can Blue-Enriched Light Keep Us Alert? PLOS ONE, 6, e16429.
https://doi.org/10.1371/journal.pone.0016429[18]

Kretschmer, V., Schmidt, K.H. and Griefahn, B. (2012) Bright Light Effects on
Working Memory, Sustained Attention and Concentration of Elderly Night Shift
Workers. Lighting Research & Technology, 44, 316-333.
https://doi.org/10.1177/1477153511418769[19]

Vetter, C., Juda, M., Lang, D., Wojtysiak, A. and Roenneberg, T. (2011) BlueEnriched Office Light Competes with Natural Light as a Zeitgeber. Scandinavian
Journal of Work, Environment & Health, 37, 437-445.
https://doi.org/10.5271/sjweh.3144[20]

Yang, J., Zhang, T., Lin, Y. and Xu, W. (2019) Effect of Illuminance and Light
Strobe on Attention and Visual Fatigue in Indoor Lighting. Proceedings of the 16th
China International Forum on Solid State Lighting & 2019 International Forum on
Wide Bandgap Semiconductors China (SSLChina: IFWS), Shenzhen, 25-27 November 2019, 149-152. https://doi.org/10.1109/SSLChinaIFWS49075.2019.9019810

[21]

[22]

DOI: 10.4236/ojtr.2024.121001

Vandewalle, G., Balteau, E., Phillips, C., Degueldre, C., Moreau, V., Sterpenich, V.,et al. (2006) Daytime Light Exposure Dynamically Enhances Brain Responses. Current Biology, 16, 1616-1621. https://doi.org/10.1016/j.cub.2006.06.031

Gola, M., Magnuski, M., Szumska, I. and Wróbel, A. (2013) EEG Beta Band Activity
Is Related to Attention and Attentional Deficits in the Visual Performance of Elderly Subjects. International Journal of Psychophysiology, 89, 334-341.
https://doi.org/10.1016/j.ijpsycho.2013.05.007

9Open Journal of Therapy and Rehabilitation