Effects of Acute Aerobic Exercise on EEG-measured Brain Activation during e-Learning

Soojin Kang; Youngjae Lee; Jonghwa Lee

doi:10.15758/ajk.2026.28.1.50

Asian J Kinesiol > Volume 28(1); 2026 > Article

Kang, Lee, and Lee: Effects of Acute Aerobic Exercise on EEG-measured Brain Activation during e-Learning

Original Research

The Asian Journal of Kinesiology 2026;28(1):50-57.

Published online: January 30, 2026

DOI: https://doi.org/10.15758/ajk.2026.28.1.50

Effects of Acute Aerobic Exercise on EEG-measured Brain Activation during e-Learning

Soojin Kang¹

, Youngjae Lee²

, Jonghwa Lee^1,^*

¹Department of Sport Science, Seoul National University of Science and Technology, Seoul, Republic of Korea

²Department of Sport Marketing, Kyungdong University, Kyunggi-do, Republic of Korea

^*Correspondence: Jonghwa Lee, Department of Sport Science, Seoul National University of Science and Technology, Gongneung-ro 232, Nowon-gu, Seoul, Republic of Korea;
Tel: +82-2- 970-6369; Email: jonghwa_lee@naver.com

Received September 29, 2025 Revised December 30, 2025 Accepted January 31, 2026

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

OBJECTIVES

The purpose of this study is to compare and analyze the effects of acute aerobic exercise on EEGmeasured brain activation during e-Learning.

METHODS

A total of 33 participants were enrolled, with 11 participants each in the control group, low-intensity aerobic exercise group, and moderate-intensity aerobic exercise group. EEG measurements were taken using the Quick-20 Dry EEG headset. The experimental task involved measuring EEG during e-Learning before the intervention and then measuring EEG during e-Learning after the intervention for each group. EEG analysis was conducted using relative power analysis per channel via power spectrum analysis. Statistical processing involved a two-way analysis of variance.

RESULTS

EEG analysis revealed significant differences only in theta waves pre- and post-test moderate-intensity aerobic exercise. However, significant differences were observed in theta waves, M-beta waves, and H-beta waves pre- and post-test acute aerobic exercise. This suggests that acute aerobic exercise activates brain waves more than moderate-intensity aerobic exercise during e-Learning.

CONCLUSIONS

Acute aerobic exercise has been shown to help activate the brain. Acute aerobic exercise before learning will activate the brain and aid learning. Therefore, it is necessary to encourage acute aerobic exercise. Furthermore, these research results can serve as basic data for developing guidelines or programs for effective learning.

Keywords: Acute Aerobic Exercise / EEG / e-Learning / Quick-20

Introduction

e-Learning is a system that enables learning anytime and anywhere using information and communication technology. Technological advancements, coupled with the pandemic situation, accelerated the shift into the e-Learning era. Currently, e-Learning is utilized across various institutions such as schools, corporations, and public enterprises. Its adoption is further expanding due to the emergence of open content like MOOCs (massive online open courses), TED (technology entertainment design), and YouTube, along with the proliferation of smart devices [1]. Reviewing prior research on EEG (EEG: Electro Encephalo Graphy) and e-Learning, a study comparing attention and academic achievement among Taiwanese students in video lecture and online lecture learning environments found higher attention levels in the video lecture environment [2]. Compared to traditional PowerPoint-based education, the video lecture environment demonstrated higher learning effectiveness and attention concentration [3]. Thus, e-Learning has proven to be an effective learning method, and education utilizing e-Learning is expected to increase in the future.

Aerobic exercise is known to be the most effective form of exercise for cardiovascular health. It not only increases oxygen uptake and the volume of blood pumped with each heartbeat [4], but also lowers resting heart rate and blood pressure, enhances cardiovascular efficiency, and reduces the prevalence of heart disease [5]. It has been revealed that aerobic exercise can aid not only in physical fitness enhancement and disease prevention but also in structural strengthening and functional improvement of the brain [6]. Furthermore, aerobic exercise facilitates the smooth supply of blood and nutrients to the brain, assists in the formation of neural networks connecting brain neurons, and particularly increases Brain-Derived Neurotrophic Factor (BDNF), thereby enhancing cognitive abilities [7]. BDNF is a central factor in brain development and growth, and its levels have been shown to increase after aerobic exercise [8].

Blood pressure rises during exercise but decreases after exercise compared to pre-exercise levels; this is called post-exercise hypotension (PEH) [9,10]. Even acute aerobic exercise alone causes a decrease in blood pressure, which can persist for up to 22 hours [11]. This blood pressure-lowering effect was particularly pronounced after 30 to 45 minutes of acute aerobic exercise at moderate intensity (50–70% HRmax) [12]. The reason to focus on blood pressure reduction through acute exercise is that persistent blood pressure reduction and acute exercise-induced blood pressure reduction cannot be explained separately [13]. Furthermore, physiological responses to acute aerobic exercise provide evidence of potential positive effects on cognitive function [14,15,16]. This includes changes in catecholamine levels, such as dopamine, as well as alterations in the concentration of neurotrophic factors like BDNF [14,16,17]. Notably, aerobic exercise at moderate intensity or higher has been shown to enhance cognitive function and activate the cerebral cortex, positively influencing brain activity [18,19].

EEG measurements are frequently used to assess brain function and state in real time. EEG represents the electrical signals generated by neural activity in the brain, detected as potential differences across the scalp. Generally, brain waves are classified into delta waves, theta waves, alpha waves, beta waves, and gamma waves according to their frequency bands. However, this study analyzed theta waves, alpha waves, and beta waves, and subdivided beta waves into SMR waves, M-beta waves, and H-beta waves for analysis. Theta waves appear frequently during light sleep and play an important role in creative thinking and learning processes. Alpha waves appear mainly in a relaxed state and are related to cognitive and emotional functions [20]. Beta waves appear in states of wakefulness, activity, and stress. SMR waves are related to concentration, and M-beta waves are predominantly expressed when absorbed in conscious activity or learning. And H-beta waves appear in situations of tension, high concentration, and high stress [20].

Meta-analyses examining aerobic exercise and EEG patterns indicate that alpha waves show the most significant increase post-exercise, with SMR waves also rising [21]. Furthermore, beta waves become activated following moderate-to-vigorous intensity exercise [22,23]. Additionally, increased theta wave activity was observed after aerobic exercise, and this increase in theta activity significantly reduced working memory errors [24]. Despite these findings, the meta-analysis results lacked consistency, making it difficult to identify clear trends. Therefore, further research is needed to understand the effects of exercise on EEG-measured brain activation [25]. Furthermore, most exercise intervention studies measure EEG only before and after the exercise session [25]. To measure the effect of exercise intervention on EEG, it is necessary to measure EEG while the individual is in the exercise situation.

Methods

Participants

Using G*POWER 3.1.9.7 software, calculations for repeated measures analysis of variance (ANOVA with Repeated Measures, within-between interaction) indicated a minimum sample size of 30 participants based on Power (1-β err prob) = 0.80, α err prob = 0.05, Effect size f = 0.3, 3 groups, and 2 measurements. Therefore, considering a 10% dropout rate, 33 participants were recruited for this study. All participants completed the study without dropping out. Participants were randomly assigned to the low-intensity aerobic exercise group (n = 11), moderate-intensity aerobic exercise group (n = 11), and control group (n = 11). Participant characteristics are shown in <Table 1>. This study was conducted with the approval of the [blinded for review] Institutional Review Board (IRB No. [blinded for review]).

Procedures

The purpose of the study, experimental tasks, experimental procedures, precautions regarding the experiment, and precautions related to personal information were explained to the participants in detail both verbally and in writing. After the participant signed the consent form, an interview was conducted regarding the participant’s health status. To confirm whether the participant had any restrictions on physical activity, the Physical Activity Readiness Questionnaire (PAR-Q) was administered. Body composition (height, weight, body mass index) was measured using a body composition analyzer (Inbody-J50, Inbody, Korea). Participants were then seated to rest, and resting heart rate was measured using a heart rate sensor (Polar H10 heart rate sensor, Polar, Finland). Target heart rates for lowintensity aerobic exercise (50-55% HRmax) and moderateintensity aerobic exercise (65-70% HRmax) were calculated using the Karvonen formula [26] to calculate the target heart rate for participants in the low-intensity aerobic exercise group (50-55% HRmax) and the moderate-intensity aerobic exercise group (65-70% HRmax). A running test was conducted on a treadmill (HERA-9000, Health One, Korea) to set individual treadmill speeds according to exercise intensity. Subsequently, the detailed experimental schedule was coordinated with the participants.

The experimental procedure involves conducting a pretest, followed by a 7-10day period, and then administering a post-test after the exercise intervention tailored to each group. Before commencing the experiment, participants were provided with a detailed explanation, both verbally and in writing, regarding the study’s purpose, experimental tasks, experimental process, precautions for the experiment, and matters related to personal information. For the pre-test, participants rested for approximately 10 minutes before wearing EEG equipment (Quick-20, Cognionics, Inc., USA) to measure EEG. Following the 10-20 international standard electrode placement, the reference electrode was attached to the left ear lobe (AL), and electrodes were placed at Fp1 (left prefrontal cortex), Fp2 (right prefrontal cortex), F3 (left frontal cortex), F4 (right frontal cortex), P3 (left parietal cortex), P4 (right parietal cortex), O1 (left occipital cortex), and O2 (right occipital cortex). Calibration was then performed. Next, participants watched a 15-minute, 38-second video titled “Psychology of Happiness” provided by the Korea Massive Open Online Course (K-MOOC). The video covered subjective well-being and the concept and research of happiness. EEG was measured during the 330 seconds of viewing the section on the concept and research of happiness. After watching the video, participants were asked to solve multiple-choice questions related to the content of the video to determine whether they had focused on the video. The questions were the same as those in the pre- and post-tests. The post-test was conducted 7-10 days later to maximize control over the priming effect. The post-test was administered after explaining the exercise intervention to the groups. The low-intensity aerobic exercise group and the moderate-intensity aerobic exercise group performed warm-up exercises, then gradually increased their speed on the treadmill to reach their individually set target speed. The treadmill speed was then adjusted to enable participants to maintain their target heart rate. Both groups maintained their target heart rate for 30 minutes and performed a cool-down after the exercise intervention. Participants rested sufficiently until their heart rate returned to resting levels. The control group received no exercise intervention. All participants had their EEG measured while watching the video at resting heart rate, using the same method as the pre-test.

EEG Analysis

EEG was measured for 330 seconds each before and after. For accurate EEG analysis, 240 seconds were analyzed, excluding the first and last 45 seconds of the 330-second period. EEG analysis was performed using BioScan software developed by Bio-tech. A bandpass filter (4-50Hz) was applied to remove noise present in the raw data. During EEG measurement, the sampling rate was set to 500Hz, the passband filter was set to 0.5-100Hz, and a fast Fourier transform (FFT) was performed. To reduce individual participant variability during measurement, EEG data underwent relative power spectrum analysis (relative power spectrum = absolute power spectrum at the corresponding frequency / total power spectrum across all frequencies) for each frequency band via power spectrum analysis.

Statistics

The independent variables in EEG analysis were the groups based on exercise intervention (control group, low-intensity aerobic exercise group, moderate-intensity aerobic exercise group) and the measurement time points (pre- and post-intervention). The dependent variables were relative power across eight frequency bands (Theta, Alpha, SMR, M-Beta, H-Beta) across 8 channels. Statistical analysis was performed using SPSS 27, with a significance level set at .05.

Results

EEG

Analysis of theta waves revealed significant differences in the left prefrontal cortex (Fp1; F = 8.655, p = 0.014) for the low-intensity aerobic exercise group, the right prefrontal cortex (Fp2; F = 6.790, p = 0.006) for the moderate-intensity aerobic exercise group, and the left parietal lobe (P3; F = 4.539, p = 0.041) and right parietal lobe (P4; F = 4.265, p = 0.048) for both low- and moderate-intensity aerobic exercise groups. (P3; F = 4.539, p = 0.041) and right (P4; F = 4.265, p = 0.048), and finally the right occipital lobe (O2; F = 1.227, p = 0.05) in the low-intensity aerobic exercise group showed statistically significant differences between pre- and post-test, but no interaction effect between group and pre-post was observed. Furthermore, the left occipital lobe of the low-intensity aerobic exercise group showed a statistically significant difference between pre- and post-test, and an interaction effect between group and pre-post was observed (O1; F = 3.509, p = 0.043). Comparing theta waves between groups revealed that the relative power of theta waves was higher in the low-intensity (F = 7.618, p = 0.01) and moderate-intensity (F = 6.717, p = 0.01) aerobic exercise groups compared to the control group, showing statistically significant differences.

Analysis of alpha waves revealed no statistically significant differences in group, pre-post, or the interaction effect between group and pre-post. Furthermore, comparing alpha waves between groups showed no statistically significant differences.

Analysis of SMR waves revealed a significant difference in relative power in the left prefrontal cortex (F = 6.096, p = 0.019) for the moderate-intensity aerobic exercise group, but no significant difference was observed in the low-intensity aerobic exercise group. Furthermore, comparing SMR waves between groups also showed no statistically significant difference.

Analysis of M-beta waves revealed a significant difference in relative power in the right occipital lobe (F = 5.022, p = 0.033) for the moderate-intensity aerobic exercise group, but no significant difference was found in the low-intensity aerobic exercise group. However, when comparing M-beta waves between groups, a significant difference was found in the moderate-intensity aerobic exercise group (F = 9.747, p = 0.004).

Analysis of H-beta waves revealed significant differences in relative power in the left (F = 5.908, p = 0.021) and right (F = 7.095, p = 0.012) prefrontal cortex regions of the moderate-intensity aerobic exercise group, but no significant differences were found in the low-intensity aerobic exercise group. However, when comparing H-beta waves between groups, a significant difference in relative power was found in the moderate-intensity aerobic exercise group (F = 4.480, p = 0.043).

The pre- and post-test comparisons of the mean relative power for each waveform across groups are shown in <Figure 1>.

Discussion

This study investigates the effects of acute aerobic exercise on EEG-measured Brain Activation during e-Learning. EEG analysis compared groups based on exercise intervention (control group, low-intensity aerobic exercise group, moderate-intensity aerobic exercise group) and measurement timing (pre- and postintervention).

While the effects of acute and chronic exercise interventions are thought to involve different mechanisms, both acute and chronic exercise are reported to exert beneficial effects on the brain [27]. Specifically, acute aerobic exercise has been shown to promote learning mechanisms and enhance cognitive task performance [28]. Furthermore, acute aerobic exercise was found to influence EEG [29]. This study also revealed distinct EEG activation patterns during e-Learning after acute aerobic exercise [27]. Specifically, theta waves were activated in both the low-intensity and moderate-intensity aerobic exercise groups. Theta waves enhance alertness, provide ideas for problem-solving, connect to creative thinking, and can generate inspiration beyond temporal and spatial constraints [30]. Furthermore, SMR waves and M-Beta waves were activated only in the moderate-intensity aerobic exercise group. SMR waves become dominant when performing relatively simple tasks requiring attention while minimizing activity in the motor sensory cortex, which is inactive during physical movement. M-Beta waves become dominant during cognitive activities involving mental load, such as calculations or mental arithmetic, where focus is concentrated on a single subject [31].

When examining brain regions, theta waves increased in the left and right prefrontal cortex and left and right parietal lobe after low- and moderate-intensity aerobic exercise. M-beta waves increased in the left frontal lobe and left and right occipital lobe after moderate-intensity exercise. Additionally, H-beta waves increased in the left and right prefrontal cortex after moderate-intensity aerobic exercise. The prefrontal cortex performs higher-order cognitive functions such as language, reasoning, and planning. The frontal lobe regulates thought processes, cognition, and movement. The occipital lobe is responsible for the initial reception of visual information and higher-order visual perception integration [32]. Therefore, brain activation following aerobic exercise may be beneficial for learning activities like e-Learning. Previous studies also reported increased theta waves in both frontal lobes after moderate-intensity aerobic exercise or higher [23], while low-intensity aerobic exercise increased theta waves in the occipital region and throughout the brain [22]. Furthermore, it was reported that task memory errors significantly decreased due to increased theta waves following aerobic exercise intervention [24]. In summary, it can be inferred that learning after aerobic exercise increases theta waves.

Physical exercise has been reported to enhance neuropsych-ological performance [33], including memory and executive function, and to increase alpha waves [34]. However, this study found that aerobic exercise did not affect the activation of alpha waves during e-Learning. This differs from previous studies in that it analyzed alpha waves holistically rather than subdividing them into slow alpha waves (8-10Hz), observed when individuals are awake but relaxed, and fast alpha waves (10-13Hz), observed during deep relaxation. Furthermore, the research task was measured in a stable state after aerobic exercise, unlike other studies, which is thought to have influenced the alpha waves.

Conclusions

This study compares and analyzes the effects of acute aerobic exercise on brain waves during e-Learning. The results indicate that acute aerobic exercise influences brain waves during e-Learning, with moderate-intensity aerobic exercise showing greater activation of brain waves during e-Learning compared to low-intensity aerobic exercise. These findings suggest that aerobic exercise may induce brain activation, potentially aiding learning. Appropriate exercise tailored to the individual during learning could activate not only the body but also the brain, thereby supporting learning. Furthermore, these results could provide foundational data for developing guidelines or programs aimed at enhancing effective learning.

This study was an experimental study conducted under limited circumstances. Therefore, the results may not be consistent across all conditions. Therefore, future research is needed, targeting diverse participants based on gender, age, intelligence, and disability, and varying the type, intensity, and duration of exercise.

Acknowledgements

This research was supported by the Regional Innovation System & Education (RISE) program through the Gangwon RISE Center, funded by the Ministry of Education (MOE) and the Gangwon State (G.S.), Republic of Korea. ([blinded for review])

Notes

Funding

This research was supported by the Regional Innovation System & Education(RISE) program through the Gangwon RISE Center, funded by the Ministry of Education(MOE) and the Gangwon State(G.S.), Republic of Korea.(2025-RISE-10-003)

Conflicts of Interest

The authors declare no conflict of interest.

Figure 1.

Comparison of pre- and post-test relative power averages between groups for each waveform.

Table 1.

Characteristics of Participants.

	Control Group	Low-intensity Aerobic Exercise Group	Moderate-intensity Aerobic Exercise Group
Age (yr)	22.82 ± 1.6	22.54 ± 1.51	22.82 ± 2.04
Height (cm)	175.45 ± 6.38	175.73 ± 5.92	176.81 ± 7.67
Weight (kg)	75.99 ± 10.71	76.01 ± 10.8	76.1 ± 8.88
BMI (kg/m)	24.69 ± 3.3	24.48 ± 2.68	24.37 ± 2.34

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