The Effect of Environmental Differences on EEG and Mood State in Bicycle Exercise

Article information

Asian J Kinesiol. 2025;27(4):108-119

Publication date (electronic) : 2025 October 31

doi : https://doi.org/10.15758/ajk.2025.27.4.108

Jonghwa Lee ¹

, Youngho Kim ²^,

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

²Department of Health and Fitness, Seoul National University of Science and Technology, Seoul, Republic of Korea

^*Correspondence: Youngho Kim, Department of Health and Fitness, Seoul National University of Science and Technology, Gongneung-ro 232, Nowon-gu, Seoul, Republic of Korea; Tel: +82-2-970-6369; E-mail: yk01@seoultech.ac.kr

Received 2025 September 8; Accepted 2025 October 13.

Abstract

OBJECTIVES

The purpose of this study is to compare and analyze the effects of environmental differences on EEG and mood states in bicycle exercise.

METHODS

A total of 60 participants took part, with 20 participants each in indoor, outdoor, and virtual reality environments. EEG was measured using the Quick-20 Dry EEG headset, and mood states were assessed using the POMS. The experimental task involved cycling at moderate intensity for 5 minutes in each environment. EEG was measured before and during exercise, while mood states were assessed before and after exercise. EEG analysis involved relative power analysis per channel using power spectrum analysis. Statistical processing employed two-way ANOVA, with Tukey HSD as the post-hoc test.

RESULTS

The exercise environment was found to influence EEG and mood states, with outdoor exercise having a more positive effect on brain waves and mood states than indoor exercise or VR exercise.

CONCLUSIONS

These findings contribute to understanding how exercise environments affect EEG and mood states and can provide foundational data for developing exercise environment guidelines and exercise or rehabilitation programs for mental health.

Keywords: Bicycle; EEG; Environment; Exercise; Mood State

Introduction

Various factors can make outdoor exercise impossible. These include changing weather, environmental pollution, and pandemics. In Korea, with its distinct four seasons, snow and rain, cold winter weather, and hot, humid summer weather make outdoor exercise difficult. Environmental pollution, such as yellow dust and fine dust, is also a factor that makes outdoor exercise challenging. Furthermore, pandemic situations like COVID-19 impacted not only indoor sports facilities but also outdoor activities where many people gather to run, cycle, hike, and more. Even in such circumstances, people exercise using treadmills, ergometers (for cycling, rowing), or engage in home workouts. Nowadays, advancements in science and technology also allow for exercising using VR. Therefore, research is needed on how differences in exercise environments affect humans.

Compared to other sports, bicycles offer greater accessibility in terms of stability, cost, and location. Already enjoyed by people worldwide as a lifestyle and leisure sport, cycling is a high-intensity aerobic exercise that provides significant physical, psychological, and mental health benefits [1]. Aerobic exercise facilitates the smooth supply of blood and nutrients to the brain, aids in the formation of neural networks connecting brain neurons, and particularly increases Brain-Derived Neurotrophic Factor (BDNF), enhancing cognitive abilities [2]. BDNF is a central factor in brain development and growth, and its levels have been shown to increase after aerobic exercise [3].

Brain research is being conducted comprehensively across various academic fields, deeply exploring human physical and mental functions. Electroencephalogram (EEG) testing is widely used to measure brain function and state in real time. EEG is an electrical signal that measures the electrical changes generated by brain neuron activity, detected via electrodes attached to the scalp surface. It allows observation of changes in brain activity over short time intervals and measurement of alterations associated with various brain functions (e.g., rest, anxiety, cognitive function, attention) [4]. Reviewing previous studies, outdoor exercise has been found to be associated with the activation of alpha waves, while VR exercise has been found to be associated with the activation of beta waves [5, 6]. Therefore, it is necessary to compare the effects of environmental differences during cycling exercise using EEG measurements.

Mood is a strong, temporary emotion accompanied by physical changes, ranging from the most positive to the most negative emotions, and can provide a specific direction, and is used interchangeably with emotion [7]. And mood state is a transient and fluctuating emotional state encompassing both positive and negative emotions [7]. Such mood states have been reported to be very positively effective for physical and mental health, as well as stress relief, when exposed to natural environments like forest bathing, similar to the effects of exercise [8,9]. Reviewing previous studies, it has been found that environmental differences during walking exercise affect mood states [10], and applying forest therapy programs, including forest walks, has been shown to improve mood states [11,12]. Therefore, comparative analysis of how environmental differences during exercise affect mood states is necessary.

This study aimed to compare and analyze the effects of environmental differences during bicycle exercise on brain waves and mood states. The results of this study, which comparatively analyze brain activation and emotions according to exercise environment, can serve as reference material for developing exercise environment guidelines and exercise or rehabilitation programs for mental health.

Methods

Participants

Using G*POWER 3.1.9.7 software, a repeated-measures ANOVA (within-between interaction) for analyzing EEG and mood states was calculated with Power (1-β err prob) = 0.90, α err prob = 0.05, Effect size f = 0.25, with 3 groups and 2 measurements. This calculation indicated a minimum requirement of 54 participants. Therefore, this study recruited 60 healthy adults, accounting for a 10% dropout rate. Participants were randomly assigned in groups of 20 to indoor, outdoor, or VR exercise groups. All participants completed the study tasks without dropouts. Participant characteristics are shown in <Table 1>. This study was approved by the Seoul National University of Science and Technology Institutional Review Board (IRB No. 2023-0023-01).

Table 1.

Characteristics of Participants.

Measurements

EEG measurement was performed using the Quick-20 Dry EEG headset (Cognionics, USA). This device is a headset that measures brainwave signals using dry-type mobile technology. Electrodes were placed in eight regions according to the 10-20 international standard electrode placement method: frontal (Fp1, Fp2), parietal (P3, P4), occipital (O1, O2), and prefrontal (Fp1, Fp2). During EEG measurement, the sampling rate was set to 500Hz. While EEG signals were being input, the frequency passband filter was set to 0.5Hz-50Hz.

To measure mood states, K-POMS was used, adapted to Korean language and culture by Kim [13]. This questionnaire consists of 65 items organized into factors established through factor analysis: Positive Emotion (7 items), Tension-Anxiety (9 items), Depression-Dejection (15 items), Anger-Hostility (12 items), Vigor-Activity (8 items), Fatigue-inertia (7 items), and Confusion-bewilderment (7 items). Responses are rated on a 5-point Likert scale: Not at all (0 points), Slightly (1 point), Moderately (2 points), Quite a bit (3 points), and Very much (4 points). Among the six POMS factors, vigor-activity represents a positive mood state, so it was reverse-coded. The average of the remaining factors was then calculated to produce the Total Mood Disturbance Score (TMDS). Therefore, higher scores on the POMS factors and TMDS indicate a more negative mood state.

Task and Procedures

The experimental task involves comparing and analyzing the effects of environmental differences during bicycle exercise on EEG and mood states. EEG was measured before and during exercise, while mood states were assessed before and after exercise. The exercise environment was defined as indoor, outdoor, or VR settings, with moderate intensity (65-74% HRmax) exercise performed in each. The exercise intensity setting referenced Park et al. [14]. The intensity for aerobic exercise was calculated using the Karvonen formula [15].

Max HR = 220 - ageTarget Heart Rate = [(Max HR - Resting HR)

The experimental procedure is as follows. Before beginning the experiment, participants were given a detailed explanation, both verbally and in writing, regarding the purpose of the study, the experimental tasks, the experimental process, precautions for the experiment, and matters related to personal information. Only volunteers who fully understood the research objectives and content, agreed to participate, and signed the consent form were allowed to participate in the experiment. First, participants wore the Polar H10 to measure heart rate and the Quick-20 Dry EEG headset to measure EEG, followed by calibration of the EEG equipment. EEG was then measured for 300 seconds starting from the point when resting heart rate was reached. After EEG measurement, a pre-exercise mood state assessment was conducted. Sufficient warm-up time was provided before measurements in indoor, outdoor, and VR settings to ensure participants could safely participate in the experiment. Participants then wore the Polar H10 and Quick-20 Dry EEG headset, and the EEG equipment was recalibrated. Depending on the exercise environment, participants rode a stationary bike mounted on a rack indoors, cycled on a dedicated bike path outdoors, or used an Oculus Quest 3 connected to a computer and linked to the Zwift application indoors to simulate an outdoor environment via VR. They wore the Oculus Quest 3 and cycled on a Zwift application freeride course. Participants were instructed to gradually increase their speed until reaching their target heart rate, then maintain that state for 300 seconds. After the EEG measurement concluded, they dismounted the bicycle, rested to stabilize, and then completed a mood state questionnaire.

EEG analysis

BioScan software developed by Bio-tech was used for EEG data analysis. Specific noise related to eye movements and electrocardiogram signals was removed using the EEG analysis program, followed by fast fourier transform. To minimize individual variations such as scalp thickness, skull thickness, electrode-skin contact quality, and differences in tension levels during measurement, the EEG data underwent relative power analysis for each channel via power spectrum analysis.

Relative Power Spectrum = Absolute Power Spectrum at that Frequency / Total Power Spectrum

Brain waves are generally classified into delta waves, theta waves, alpha waves, beta waves, and gamma waves based on their frequency bands. In this study, the frequency bands were subdivided into theta waves (4-8Hz), alpha waves (8-13Hz), slow alpha waves (8-10Hz), fast alpha waves (10-13Hz), beta waves (13-30Hz), low beta waves (13-15Hz), mid beta waves (15-20Hz), high beta waves (20-30Hz), gamma waves (30-50Hz), and SMR (12-15Hz) waves [16].

Statistics

This study compares and analyzes the effects of environmental differences on EEG and mood states during bicycle exercise. The independent variables for EEG analysis are environment (indoor, outdoor, VR groups) and measurement timing (pre-exercise, during exercise), with EEG as the dependent variable. For mood state, the independent variables are environment (indoor, outdoor, VR groups) and measurement timing (pre-exercise, post-exercise), with mood state factors (Tension-Anxiety, Depression-Dejection, Anger-Hostility, Vigor-Activity, Fatigue-Inertia, Confusion-Bewilderment). Post-hoc tests used Tukey’s HSD. All statistical analyses were performed using SPSS 28, with a statistical significance level set at.05.

Results

EEG

The results of a comparative analysis examining the effects of environmental differences on EEG in bicycle exercise are as follows <Table 2>, <Figure 1>. In theta waves, no significant differences were observed for the interaction effect between environmental differences and pre-exercise/during exercise [F=2.520, p>0.05, η_p²=0.042], nor for the main effects of pre-exercise/during exercise [F=3.373, p>0.05, η_p²=0.020]. However, a significant main effect was observed for environmental differences [F=3.932, p<0.05, η_p²=0.065]. Post-hoc tests revealed significant differences between the outdoor environment group and the VR environment group in the environmental differences factor. In the alpha waves, significant differences were observed for the interaction effect between environmental conditions and pre- and during-exercise [F=4.533, p<0.05, η_p²=0.074], and for the main effect of environmental conditions [F=7.974, p<0.01, η_p²=0.123]. However, no significant difference was found for the main effect of pre- and during-exercise [F=2.360, p>0.05, η_p²=0.020]. Post-hoc tests revealed significant differences in environmental conditions between the indoor environment group and the outdoor environment group, and between the outdoor environment group and the VR environment group. In slow alpha waves, significant differences were observed for the interaction effect between environmental difference and pre/during exercise [F=8.755, p<0.001, η_p²=0.133], and the main effect of environmental difference [F=8.605, p<0.001, η_p²=0.131]. However, the main effect of pre/during exercise did not show a significant difference [F=1.578, p>0.05, η_p²=0.014]. Post-hoc tests revealed significant differences in environmental difference between the indoor environment group and the outdoor environment group, and between the outdoor environment group and the VR environment group. In fast alpha waves, significant differences were found in the interaction effect between environment and pre/during exercise [F=8.465, p<0.001, η_p²=0.129], the main effect of environment [F=4.910, p<0.01, η_p²=0.079], and the main effect of pre/during exercise [F=9.373, p<0.01, η_p²=0.076]. Post-hoc tests revealed significant differences in environment between the outdoor environment group and the VR environment group. For beta waves, significant differences were found in the interaction effect between environmental differences and pre/during exercise [F=4.937, p<0.01, η_p²=0.080], and the main effect of environmental differences [F=4.405, p<0.05, η_p²=0.072]. However, no significant difference was found in the main effect of pre/during exercise [F=1.739, p>0.05, η_p²=0.015]. Post-hoc tests revealed significant differences in environmental differences between the indoor environment group and the outdoor environment group, and between the indoor environment group and the VR environment group. In the low beta waves, significant differences were found in the interaction effect between environmental difference and pre/during exercise [F=9.853, p<0.001, η_p²=0.147], the main effect of environmental difference [F=12.307, p<0.001, η_p²=0.178], and the main effect of pre/during exercise [F=17.054, p<0.001, η_p²=0.130]. Post-hoc tests revealed significant differences in environmental difference between the indoor environment group and the outdoor environment group, and between the outdoor environment group and the VR environment group. In the medium beta waves, significant differences were found in the interaction effect between environmental difference and pre/during exercise [F=13.599, p<0.001, η_p²=0.193] and the main effect of environmental difference [F=6.939, p<0.01, η_p²=0.109]. However, the main effect of pre- and during-exercise did not show a significant difference [F=2.560, p>0.05, η_p²=0.022]. Post-hoc tests revealed significant differences in environmental differences between the indoor environment group and the outdoor environment group. In the high beta waves, no significant differences were found in the interaction effect between environmental differences and pre- and during-exercise [F=2.275, p>0.05, η_p²=0.038], or in the main effect of environmental differences [F=2.225, p>0.05, η_p²=0.038]. However, the main effect of pre- and during-exercise showed a significant difference [F=8.421, p<0.01, η_p²=0.069]. In gamma waves, significant differences were observed for the interaction effect between environmental difference and pre/during exercise [F=11.386, p<0.001, η_p²=0.166], and the main effect of environmental difference [F=10.986, p<0.001, η_p²=0.162]. However, no significant difference was observed for the main effect of pre/during exercise [F=2.031, p>0.05, η_p²=0.018]. Post-hoc tests revealed significant differences in environmental difference between the indoor environment group and the outdoor environment group, and between the outdoor environment group and the VR environment group. For SMR waves, significant differences were observed in the interaction effect between environmental differences and exercise before/during [F=8.041, p<0.01, η_p²=0.124], and the main effect of environmental differences [F=6.937, p<0.01, η_p²=0.108]. However, no significant difference was observed for the main effect of exercise before/during [F=2.880, p>0.05, η_p²=0.025]. Post-hoc tests revealed significant differences in environmental differences between the outdoor environment group and the VR environment group.

Table 2.

Mean differences in EEG according to environmental differences and before and during bicycle exercise.

Figure 1.

Mean differences in EEG according to environmental differences and before and during bicycle exercise.

Mood State

The results of a comparative analysis examining how environmental differences during cycling exercise affect mood states are as follows <Table 3>, <Figure 2>. For Tension-Anxiety, significant differences were found in the interaction effect between environmental differences and pre- vs. post-exercise [F=5.991, p<0.01, η_p²=0.095], the main effect of environmental differences [F=14.215, p<0.001, η_p²=0.200], and the main effect of pre- vs. post-exercise [F=20.888, p<0.001, η_p²=0.155]. Post-hoc tests revealed significant differences between the indoor environment group and the outdoor environment group, and between the indoor environment group and the VR environment group. For Depression-Dejection, significant differences were found in the interaction effect between environment and pre/post exercise [F=4.044, p<0.05, η_p²=0.066], the main effect of environment [F=8.929, p<0.05, η_p²=0.135], and the main effect of pre/post exercise [F=54.462, p<0.05, η_p²=0.323]. Post-hoc tests revealed significant differences in environment between the indoor environment group and the outdoor environment group, and between the indoor environment group and the VR environment group. For Anger-Hostility, significant differences were found in the interaction effect between environment and pre/post exercise [F=5.877, p<0.05, η_p²=0.093], the main effect of environment [F=13.567, p<0.05, η_p²=0.192], and the main effect of pre/post exercise [F=58.862, p<0.05, η_p²=0.341]. Post-hoc tests revealed significant differences in environment between the indoor environment group and the outdoor environment group, and between the indoor environment group and the VR environment group. For Vigor-Activity, significant differences were found in the interaction effect between environment and pre- vs. post-exercise [F=6.371, p<0.05, η_p²=0.101], the main effect of environment [F=4.471, p<0.05, η_p²=0.073], and the main effect of pre- vs. post-exercise [F=18.791, p>0.001, η_p²=0.142]. Post-hoc tests revealed significant differences in environment between the indoor environment group and the outdoor environment group, and between the indoor environment group and the VR environment group. For Fatigue-Inertia, significant differences were found in the interaction effect between environment and pre/post exercise [F=10.401, p<0.05, η_p²=0.154], the main effect of environment [F=12.686, p<0.05, η_p²=0.182], and the main effect of pre/post exercise [F=6.821, p<0.05, η_p²=0.056]. Post-hoc tests revealed significant differences in environment between the indoor environment group and the outdoor environment group, and between the indoor environment group and the VR environment group. For Confusion-Bewilderment, significant differences were found in the interaction effect between environment and pre/post exercise [F=5.893, p<0.05, η_p²=0.094], the main effect of environment [F=9.706, p<0.05, η_p²=0.146], and the main effect of pre/post exercise [F=23.555, p<0.05, η_p²=0.171]. Post-hoc tests revealed significant differences in environment between the indoor environment group and the outdoor environment group, and between the indoor environment group and the VR environment group. For the TMDS, significant differences were found in the interaction effect between environmental differences and pre-and post-exercise [F=10.324, p<0.001, η_p²=0.153], the main effect of environmental differences [F=16.147, p<0.001, η_p²=0.221], and the main effect of pre- and post-exercise [F=42.957, p<0.001, η_p²=0.274]. Post-hoc tests revealed significant differences in environmental differences between the indoor environment group and the outdoor environment group, and between the indoor environment group and the VR environment group.

Table 3.

Mean differences in Mood State according to environmental differences and before and after bicycle exercise.

Figure 2.

Mean differences in Mood State according to environmental differences and before and after bicycle exercise.

Discussion

This study aims to compare and analyze the effects of environmental differences on EEG and mood states in bicycle exercise. EEG measurement results were compared and analyzed based on exercise environment (indoor, outdoor, VR) and measurement timing (pre-exercise, during exercise). Mood state measurement results were compared and analyzed based on exercise environment (indoor, outdoor, VR) and measurement timing (pre-exercise, post-exercise).

The environment in which one exercises can be as important as the exercise itself [17,18]. Exercising in outdoor natural settings provides greater benefits to the brain than exercising indoors [19]. Previous research has shown that outdoor exercise is associated with lower perceived exertion levels during exercise and increased alpha wave activity in the prefrontal cortex, while VR exercise has been found to activate beta waves more than indoor exercise [5,6]. In this study as well, the outdoor exercise group showed greater activation of theta and alpha waves during exercise. Theta waves contribute to emotional functions such as creative thinking, emotional stability, emotion processing, and stress reduction, and are dominant during meditation or rest [20]. Alpha waves are associated with relaxation of mental and physical tension, stress relief, and improved concentration and memory [21]. They are gaining attention as a method for maintaining positive mental and physical health and treating various mental disorders such as alienation, depression, and stress [22]. This study also showed that outdoor exercise and VR exercise activated more beta waves than indoor exercise. Beta waves can be subdivided into low, medium, and high beta waves. They appear during states of arousal, activity, and stress, and are also influenced by auditory, tactile, and emotional stimuli [23]. Low beta waves are associated with alertness, readiness, or the motor system’s standby state, linked to focused attention [24]. Mid beta waves dominate during conscious activities or when deeply engaged in mental tasks or learning [23]. High beta waves, however, appear during states of tension, excitement, or stress. Gamma waves occur during heightened states of anxiety or excitement, associated with heightened external awareness [23]. Specific research findings indicate that outdoor exercise activates lower beta and mid -beta waves more than indoor exercise or VR exercise. However, high beta and gamma waves were most activated during VR exercise and least activated during outdoor exercise. In summary, the outdoor exercise group showed more positive outcomes than the indoor exercise group and the VR group. These findings align with evidence that the combination of physical activity and nature exposure provides greater psychological and physiological benefits than physical activity alone [25]. They also generally agree with conceptual analyses suggesting that digitally mediated nature experiences, like VR, can offer psychophysiological benefits similar to, though less intense than, those observed in actual nature experiences, depending on fidelity and immersion levels [26].

Green Exercise, which combines physical activity with exposure to nature, is known to benefit both physical and mental health [27]. VR exercise has been shown to have positive physiological, psychological, and rehabilitative effects compared to traditional exercise [28]. Reviewing prior studies, research comparing outdoor green cycling groups with indoor stationary cycling groups showed that outdoor green cycling positively influenced mood states [29]. Additionally, studies comparing groups cycling in VR environments with those cycling indoors found that the VR cycling group experienced a more positive improvement in mood states [30]. In summary, overall, mood states improved more positively after outdoor exercise and VR exercise than after indoor exercise. Therefore, it can be concluded that outdoor exercise or VR exercise, rather than indoor exercise, helps positively change mood states.

Physical activity in nature can lead to clearer and additional mental health benefits, such as lowering stress and anxiety levels and improving overall psychological well-being [31]. A systematic review of the literature emphasizes that nature positively impacts cognition function and mental health [32]. Indeed, prior research has demonstrated that people find exercising in nature more enjoyable than exercising indoors [33,34]. This study also showed, through EEG and mood state measurements, that outdoor exercise is more effective for mental health than indoor exercise or VR exercise. However, when outdoor exercise isn’t possible, we must seek alternatives. One such alternative is VR exercise. The results clearly indicate that VR exercise is more effective than indoor exercise. Yet, it still falls short of the benefits of outdoor exercise. From the perspective of Ecological Dynamics Theory, a modern framework studying self-organizing phenomena within the performer-environment system, an organism’s behavior is linked to its physically extended environment [35]. Therefore, with the advancement of VR-related science and technology, if a VR environment can be created that mimics the actual outdoor environment, it is expected to yield effects similar to outdoor exercise.

Conclusions

This study aims to compare and analyze the effects of environmental differences during cycling exercise on EEG and mood states. The results indicate that the exercise environment influences EEG and mood states, with outdoor exercise having a more positive effect on both than indoor exercise or VR exercise. These findings will aid in understanding the effects of exercise environments on brain waves and mood states. They can also serve as foundational data for developing exercise environment guidelines for mental health and designing exercise or rehabilitation programs.

Notes

Acknowledgments

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2023S1A5B5A17084101).

The authors declare no conflict of interest.

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Groups	Participants (M/F)	Age (years)	Height (cm)	Weight (kg)
Indoor	20(16/4)	21.85±2.25	172.40±6.92	70.11±12.45
Outdoor	20(14/6)	21.80±2.21	170.85±7.90	68.76±11.05
VR	20(17/3)	21.80±2.25	172.37±7.46	70.49±12.14

Frequency	Groups	Before Exercise	After Exercise	F(p)	Post-hoc
Theta	Indoor^a	0.0799±0.0325	0.0902±0.0676	2.520	b>c
	Outdoor^b	0.0836±0.0341	0.1178±0.0450
	VR^c	0.0784±0.0330	0.0968±0.0322
Alpha	Indoor^a	0.1672±0.0825	0.1365±0.0578	4.533^*	b>a,c
	Outdoor^b	0.1707±0.0546	0.1993±0.0480
	VR^c	0.1563±0.0690	0.1080±0.0354
Slow Alpha	Indoor^a	0.0694±0.0264	0.0587±0.0267	8.755^***	b>a,c
	Outdoor^b	0.0685±0.0222	0.0878±0.0303
	VR^c	0.0686±0.0239	0.0432±0.0129
Fast Alpha	Indoor^a	0.1214±0.0430	0.0918±0.0227	8.465^***	b>c
	Outdoor^b	0.1093±0.0430	0.1237±0.0193
	VR^c	0.1129±0.0357	0.0796±0.0149
Beta	Indoor^a	0.3861±0.0247	0.3573±0.0269	4.938^**	b,c>a
	Outdoor^b	0.3855±0.0322	0.3912±0.0247
	VR^c	0.3847±0.0345	0.3881±0.0181
Low Beta	Indoor^a	0.0534±0.0098	0.0520±0.0076	9.853^***	b>a,c
	Outdoor^b	0.0533±0.0060	0.0677±0.0081
	VR^c	0.0505±0.0074	0.0555±0.0084
Mid Beta	Indoor^a	0.1101±0.0106	0.0969±0.0079	13.599^***	b>a
	Outdoor^b	0.1071±0.0090	0.1193±0.0072
	VR^c	0.1121±0.0214	0.1030±0.0074
High Beta	Indoor^a	0.2262±0.0204	0.2138±0.0243	2.275
	Outdoor^b	0.2288±0.0273	0.1994±0.0252
	VR^c	0.2288±0.0298	0.2259±0.0380
Gamma	Indoor^a	0.3443±0.0788	0.3971±0.0920	11.386^***	a,c>b
	Outdoor^b	0.3550±0.0486	0.2897±0.0623
	VR^c	0.3586±0.0652	0.4240±0.0471
SMR	Indoor^a	0.0886±0.3412	0.0793±0.0120	8.041^**	b>c
	Outdoor^b	0.0819±0.0135	0.1047±0.0120
	VR^c	0.0768±0.0130	0.0801±0.0126

Scales	Groups	Before Exercise	After Exercise	F(p)	Post-hoc
Tension-Anxiety	Indoor^a	1.65±0.279	1.66±0.728	5.991^**	a>b,c
	Outdoor^b	1.42±0.296	0.95±0.421
	VR^c	1.55±0.323	0.91±0.422
Depression-Dejection	Indoor^a	1.31±0.418	1.00±0.922	4.044^*	a>b,c
	Outdoor^b	1.14±0.231	0.26±0.365
	VR^c	1.23±0.399	0.38±0.398
Anger-Hostility	Indoor^a	1.34±0.375	1.10±0.824	5.877^**	a>b,c
	Outdoor^b	1.14±0.231	0.26±0.365
	VR^c	1.23±0.399	0.58±0.672
Vigor-Activity	Indoor^a	1.94±0.472	2.02±0.898	6.371^***	a>b,c
	Outdoor^b	2.01±0.341	1.24±0.449
	VR^c	2.01±0.451	1.31±0.719
Fatigue-Inertia	Indoor^a	1.64±0.459	2.05±0.999	10.401^***	a>b,c
	Outdoor^b	1.45±0.413	0.91±0.451
	VR^c	1.81±0.535	1.09±0.500
Confusion-Bewilderment	Indoor^a	1.61±0.382	1.61±0.888	5.893^**	a>b,c
	Outdoor^b	1.46±0.373	0.77±0.342
	VR^c	1.62±0.415	0.98±0.414
TMDS	Indoor^a	1.58±0.286	1.57±0.706	10.324^***	a>b,c
	Outdoor^b	1.44±0.244	0.73±0.307
	VR^c	1.57±0.313	0.84±0.383