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ISSN : 1229-6457(Print)
ISSN : 2466-040X(Online)
The Korean Journal of Vision Science Vol.27 No.4 pp.389-402
DOI : https://doi.org/10.17337/JMBI.2025.27.4.389

Electroencephalogram (EEG) Changes Following HMD-VR and their Correlation with Motion Sickness Susceptibility

In-Sun Park1), Jung Un Jang2)
1)Dept. of Optometry, Graduate School, Eulji University, Student, Uijeongbu
2)Dept. of Optometry, Eulji University, Professor, Seongnam
* Address reprint requests to Jung Un Jang (https://orcid.org/0000-0002-2475-315X) Dept. of Optometry, Eulji University, Seongnam TEL: +82-31-740-7491, E-mail: jju@eulji.ac.kr
December 4, 2025 December 23, 2025 December 23, 2025

Abstract


Purpose : To study aimed to quantitatively characterize in the electroencephalogram (EEG) changes induced by head-mounted display virtual reality (HMD-VR) and to exame the association between VR-related EEG changes and motion sickness susceptibility, assessed using the Motion Sickness Susceptibility Questionanaire (MSSQ), better understand cybersickness.



Methods : Resting-state EEG was recorded from 11 adult women before and after VR use using a DSI-24, dry electrode headset. Motion sickness susceptibility was measured through the Motion Sickness Susceptibility Questionnaire (MSSQ) survey. EEG signals from 19 channels were processed to obtain relative power across nine frequency bands, and were analyzed using the Wilcoxon signed-R\rank Test, Spearman’s correlation analysis, and Mann-Whitney U Test.



Results : After VR use, relative theta power (RT) decreased while attention-related indices (RST, RMT, RSMT) increased, particularly in the occipital lobe. Correlation analysis revealed significant association between MSSQ scores and EEG signals in the temporal lobe (T6) and parietal lobe (Pz) regions. The high susceptibility group showed a significant decrease in parietal beta-band relative power (RLB, RB, RMB) at (Pz, P3, and P4), whereas the low susceptibility group showed an increase (p<0.050).



Conclusion : The post-VR increase in attention-related ratio indices in the occipital lobe may reflect enhanced visual stimuli processing during VR exposure. In contrast, the opposing beta-band patterns observed in the parietal lobe, support the hypothesis that sensory conflict between visual input and bodily sensations in VR contributes to cybersickness.


Cybersickness , EEG , HMD-VR , MSSQ

HMD 기반 가상현실 사용에 따른 뇌파(EEG)의 정량적 분석과 멀미 민감성과의 상관관계 연구

박인선1), 장정운2)
1)을지대학교 대학원 안경광학과, 학생, 의정부
2)을지대학교 안경광학과, 교수, 성남

    Ⅰ. Introduction

    Virtual reality (VR) can offer new experiences and enhance user engagement through immersive visual stimulation. However, it can also cause cyber sickness such as nausea, disorientation, and eye fatigue.1) The symptoms of cyber sickness are about three times more severe than those of general simulation sickness, and the underlying mechanisms of these two types of motion sicknesses differ.2) To alleviate this discomfort, several studies have investigated factors that influence the experience of using Head-Mounted Display Virtual Reality (HMD-VR). These factors include hardware specifications such as resolution, latency, and flicker, visual content elements like optical flow,3-5) VR fidelity, Visual components of content6-14) and user characteristics including demographic factors, prior VR experience, and interaction style with VR5,15-17 have been reported to influence cyber sickness. Early research focused on hardware performance improvements and studies considering user characteristics15,18,19). However, despite the high-resolution display specifications of the latest VR systems, symptoms of cyber sickness are still frequently reported.20,21) Research indicates that increasing resolution does not significantly affect user experience or motion sickness levels,22) suggesting that factors other than resolution may also influence motion sickness. Regarding user characteristics, recent trends indicate that the relative importance of factors contributing to cyber motion sickness is gradually decreasing, while the proportion of studies focusing on content-related aspects is steadily increasing.9,19)

    The discrepancy between visual cues in the VR environment and the vestibular system has been reported as a primary cause of motion sickness.21,23) This theory explains that sensory conflict occurs when a user's actual movements differ from the visual information presented in virtual reality, causing symptoms such as motion sickness.24) To address these sensory mismatch issues, various studies are underway, including providing tactile feedback through wearable devices and employing hardware solutions such as treadmill floor systems that physically move the body to align sensory input25,26). Despite these efforts, reports of cyber sickness persist. It is possible that not only the existing visual-vestibular mismatch but also the discrepancy between the visual information provided in VR environments and the natural human field of view contribute to this issue. Recent HMD VR devices offer a high-resolution display with an even wider field of view (FOV). They provide a field of view angle approaching that of human binocular vision, and the highspecification display options themselves may be a cause of motion sickness.27,28) The range of binocular vision within the total human field of view is approximately 120 to 140 degrees. However, the area where clear discrimination is generally possible when looking straight ahead is roughly 60 degrees, and the central field of vision is considerably narrower than that.29,30) The area beyond that is processed at a lower resolution rather than as precise images, functioning as peripheral vision that detects movement. The human brain routinely and unconsciously compensates for this peripheral vision information.29,31) However, the visual information provided by VR environments differs from the way human visual processes information, as VR offers an environment with high-resolution images at all viewing angles and a high level of texture detail.8) In virtual reality, high levels of immersion and detailed content are achieved by creating virtual environments that closely resemble real-world settings. Previous research supports this, indicating that higher VR fidelity is associated with increased severity of motion sickness.10,32) When these factors combine to disrupt visual processing, it can be inferred that users experience a sense of disconnection and ultimately exhibit symptoms of motion sickness.20,33) Additionally, VR stereoscopic images are influenced by user characteristics. Users who are sensitive to motion sickness experience greater discomfort with 3D images than with 2D images, exhibit higher SSQ scores compared to other groups, and take less time to feel nauseous.34-36)

    Considering these research findings, differences in cyber sickness may arise depending on an individual's susceptibility to motion sickness. Therefore, interactions between the visual elements of VR content and user characteristics are likely. Following single-factor analysis, it is necessary to collect and analyze motion sickness data occurring during VR use to investigate interactions between these factors. Until now, self-report questionnaire methods have primarily been used to analyze physiological changes that occur after using VR devices, and the most widely used tool among these is SSQ (Simulator Sickness Questionnaire) developed by Kennedy.37) While this method has the advantage of being simple, it is limited by its reliance on subjective data. To address this limitation, objective evaluation methods using physiological signals, such as electroencephalogram (EEG) and electrocardiogram (ECG), are now being used in parallel.38-41) Quantitative electroencephalogram classifies measured brainwave signals by frequency band and uses them as indicators reflecting the brain's functional state related to psychological and arousal states.42,43) During EEG measurement, electrodes for signal acquisition are placed according to the standard electrode placement method, arranged to correspond to specific regions of the cerebral cortex.44) Previous studies have utilized these characteristics to analyze brain activity and the functions performed by the cerebral cortex in specific situations.45-49) These objective metrics are used to supplement individual users' subjective reports, but they may also carry the limitations inherent in existing subjective measurements. Typically, subjective measurements involve a time lag, as users read and respond to questionnaires only after HMD VR use has completely ended. EEG measurements involve attaching electrodes to the scalp to record brain electrical activity. This process requires removing the HMD VR, attaching the electrodes, and then measuring the EEG, resulting in a time delay. This study aims to measure changes in relative EEG power following VR use with minimal delay using a dry EEG device capable of simultaneously measuring brain waves while wearing an HMD VR headset. The objectives are to quantitatively analyze relative power changes during HMD VR use and to determine the correlation between EEG metrics and subjective motion sickness scales (MSSQ, SSQ).

    Ⅱ. Methods

    1. Subjects

    The study subjects were 11 healthy young adult women who voluntarily participated in this research. They received thorough verbal and written explanations regarding the study objectives and examination methods and provided written informed consent. Selection criteria included no history of ophthalmic disease, no issues with near vision, and a refractive error difference between both eyes of less than 2.00 D. For subjects with refractive errors, experiments were conducted after full correction using contact lenses suitable for HMD VR wear. All examination procedures and protocols in this study were approved by the Institutional Review Board.

    2. Method

    Participants completed the Motion Sickness Susceptibility Questionnaire (MSSQ) and had their pre-test EEG measured in a resting state before using the VR system. They then wore an HMD VR device and used VR content consisting of the games ‘Thief Simulator VR’ and ‘Epic Roller Coasters’— which provide highly immersive environments, for approximately 20 minutes (including calibration time between each content). Immediately after use, post-test resting EEG was measured again during a 3-minute resting period. Finally, participants completed the Simulator Sickness Questionnaire (SSQ) to assess subjective motion sickness levels. The entire experiment, including questionnaires and rest periods, lasted approximately 50 minutes.

    3. Data Analysis

    Electroencephalogram (EEG) recordings, used as an objective measure of motion sickness, were obtained with the 21-channel wireless dry EEG system Dry Sensor Interface-24 (DSI-24, Wearable Sensing, San Diego, USA). This device can be worn simultaneously with a head-mounted display (HMD) for virtual reality (VR), allowing EEG recording during VR use. This setup facilitated the use of the EEG cap. This enabled the measurement of post- stabilization EEG immediately after VR use, following a brief calibration process, without delays caused by separate removal procedures (Table 1).Following the 10-20 system standard for electrode placement, a total of 19 sites (Fp1, Fp2, Fz, F3, F4, F7, F8, Cz, C3, C4, T3, T4, T5, T6, P3, P4, Pz, O1, O2) using dry Caps. Reference electrodes A1 and A2 were attached to the earlobes for unipolar recording (Table 2). Among these, the Fp1 and Fp2 areas were adjusted upward to accommodate VR equipment wear, and the data were analyzed post-experiment. The output EEG signal CSV file was analyzed using the time-series analysis program Telescan (TeleScan Ver. 3.29, LAXTHA Incorporated, Daejeon, Korea). A fast Fourier transform (FFT) was applied to obtain the absolute power spectrum for each frequency band. To minimize measurement factors such as individual differences in skull shape, variations in electrical resistance due to scalp condition, and tension levels the data were calculated and utilized as relative power. The collected EEG data were analyzed using ratio theta (RT, 4~8 Hz, hereafter RT), ratio alpha (RA, 8~13 Hz, hereafter RA), ratio beta (RB, 13~30 Hz, hereafter RB), ratio low beta (RLB, 12~15 Hz, hereafter RLB), ratio mid beta (RMB, 15~20 Hz, hereafter RMB), ratio high beta (RHB, 20~30 Hz, hereafter RHB), ratio of sensorimotor rhythm to theta (RST, 12~15 Hz, hereafter RST), ratio of mid beta to theta power (RMT, 15~20 Hz, hereafter RMT), and ratio of SMR to mid-beta to theta power (ratio of SMR–mid beta to theta power, RSMT, 12~20 Hz, hereafter RSMT). The interpretation of EEG signals corresponding to each indicator is shown in Tables 1 and 2. Statistical analysis of EEG data, extracted from 19 scalp locations, was performed using IBM SPSS Statistics 21.00Ⓡ and R: A Language and Environment for Statistical Computing. The statistical tests included the Wilcoxon signed-rank test, Spearman correlation analysis, and the Mann-Whitney U test.

    Ⅲ. Result

    1. Brainwave Testing for VR-Induced Cyber Sickness Before and After

    As a result, overall low-frequency band (RT) indicators showed a significant decrease in the parietal lobe (Cz, C3), temporal lobe (T6), and parietal lobe (P3) regions in post EEG compared to pre EEG. and the sensory-motor rhythm power indicators relative to theta waves (RST, RMT, RSMT) showed a significant increase in most regions outside the temporal lobe, including the prefrontal cortex (Fp1), frontal cortex (F7), parietal cortex (P3), occipital cortex (O1, O2), and occipital lobe (O1, O2) (p<0.050)(Table 3).

    2. Correlation between changes in EEG indicators and motion sickness susceptibility

    Spearman's correlation analysis was performed to confirm the correlation between the change in EEG indicators (ΔEEG) and motion sickness susceptibility questionnaire (MSSQ) scores. The results showed that higher motion sickness susceptibility (MSSQ) scores were significantly positively correlated with the change in beta waves (RB) at the temporal lobe T6 channel (r=0.610, p<0.050) and significantly negatively correlated with the change in theta waves (RT) at the parietal lobe Pz channel (r=-0.620, p<0.050). The occipital O2 channel showed a significant positive correlation with the alpha waves (RA) change (r=0.620, p<0.050) and a significant negative correlation with the high-beta waves (RHB) change as adult motion sickness susceptibility (TMSB) increased (r = -0.640, p<0.050)(Table 4).

    3. Cyber Sickness Sensitivity Based on Changes in Brain wave Indicators

    The results showed a significant difference in relative beta wave changes between the low MSSQ group (M=0.038) and the high MSSQ group (M=-0.065) (p<0.050). The median and quartile values of relative beta wave changes after VR use were significantly lower in the group with high motion sickness sensitivity. Relatively low beta wave changes showed a significant difference between the low MSSQ group (M=0.007) and the high MSSQ group (M=-0.015) (p<0.050). In the group with high motion sickness susceptibility, the median relative low-beta wave change after VR use was significantly lower. The relative mid-beta wave change showed a significant difference between the low MSSQ group (M=0.019) and the high MSSQ group (M=-0.042) (p<0.050). The median relative mid-beta wave change after VR use was significantly lower in the group with high motion sickness susceptibility (Table 5).

    4. RB ch3 EEG Index Change and Motion Sickness Sensitivity

    Analysis of the relationship between the change in EEG indicators (RB ch3) and motion sickness susceptibility levels showed no statistically significant effect in either the linear model or the cubic nonlinear model. Statistically, the most suitable model was the quadratic nonlinear model (S.E.E.=.097, R²= .546). While the sensitivity level of the first-order function had no significant effect, a quadratic curve relationship was confirmed where the second-order function exerted a significant influence (β=1.750, p<0.050) (Table 6).

    5. RB ch17 Changes in EEG Indicators and Motion Sickness Sensitivity

    An analysis of the relationship between the change in brainwave indicators (RMB ch17) and motion sickness sensitivity levels revealed no statistically significant effects in either the linear or quadratic nonlinear models. The statistically optimal model is the cubic nonlinear model (S.E.E.=.018, R²=.843). A cubic curve relationship was confirmed, where the first-order (β=6.958, p<0.010), second-order (β=-22.124, p<0.010), and third-order (β=15.670, p<0.010) functions all exerted significant effects (Table 7).

    6. RHB ch3 EEG Index Change and Motion Sickness Sensitivity

    An analysis of the relationship between the change in EEG indicators (RHB ch3) and motion sickness susceptibility levels revealed no statistically significant effects in the cubic nonlinear model. Significant effects were observed in both the linear and quadratic nonlinear model (Quadratic). Among the models, the quadratic nonlinear model was statistically the most suitable (S.E.E.=.064, R²=.690). While the linear function showed no significant effect on motion sickness sensitivity levels, a quadratic relationship was confirmed where the quadratic function exerted a significant influence (β=1.725, p<0.050) (Table 8).

    Ⅳ. Discussion

    This study analyzed the effects of HMD VR use on resting-state EEG and investigated whether the magnitude of these changes correlates with individual motion sickness susceptibility (MSSQ). First, we examined whether VR use affected brain activity. Low-frequency activity, such as theta waves (RT), consistently decreased across the brain's central, temporal, parietal, and occipital lobes. Conversely, high-frequency indicators related to attention focus, such as the ratio of beta waves to theta waves (RMT, RSMT), increased in the prefrontal and occipital lobes. The ratio of theta waves to the corresponding beta-band indicators in the sensory-motor rhythm suggests that while theta waves did not increase, the SMR band did. This phenomenon indicates a shift toward a state of heightened attention and stress, meaning the proportion occupied by relaxation and stability decreased, while the proportion occupied by focus on something or an aroused state increased. These indicators showed an overall increase, indicating that VR use induces a state of concentration in the prefrontal and occipital regions. However, it should be noted that the fp1 and fp2 regions, corresponding to the prefrontal cortex, are very close to the eyes. Although the values were corrected and used, they may still be affected by eye movements. In the frontal lobe (F3) and occipital lobe (O1, O2) regions, the concentrationrelated indicators relative high-beta (RHB) and relative low-beta (RLB) decreased. This can be interpreted as indicating a state where stable concentration in the relatively lower frequency bands, compared to the high-frequency bands, is being disrupted. Previous studies indicated that theta waves showed increased activity in the occipital lobe and decreased activity in the frontal lobe region. Alpha waves exhibited increased output in the parietal lobe region. Beta waves show increased activity in all brain regions in a 2D environment, whereas in a 3D environment, they may be induced by specific movements50). The patterns of EEG changes showed significant differences depending on motion sickness susceptibility. We aimed to examine the interaction between the amount of EEG changes measured after VR use and the personal characteristic of motion sickness susceptibility. Among the subscales of motion sickness susceptibility, adultonset motion sickness susceptibility (TMSB) showed the most significant correlations. In the occipital lobe region (O2), which corresponds to the visual center, higher motion sickness sensitivity correlates with increased changes in the low-frequency band (RA), while conversely, changes in the relatively high-beta band (RHB) tend to decrease. Meanwhile, in the parietal lobe region (Pz), processing somatosensory information, both theta waves (RT) and relatively low-beta waves (RLB) showed a tendency for the amount of change to decrease as motion sickness sensitivity (MSSQ) increased. The results of the correlation between the visual cortex and the brain regions processing bodily sensory information differed. To support these findings, further research is needed to examine the interaction between visual information and bodily sensory information. When comparing brainwave changes between low-sensitivity and high-sensitivity groups based on motion sickness susceptibility, significant differences were observed in the beta band (RLB, RB, RMB) within the parietal lobe regions (Pz, P3, P4). Previous studies using power analysis, SSQ, and FMS found that cyber sickness severity increased with greater visual angle, and responses were observed in the frontal lobe, parietal lobe, and central occipital lobe51). The parietal lobe integrates sensory information such as visual, vestibular, and proprioceptive inputs. In the motion sickness-sensitive group, beta wave indicators related to concentration showed a significant decrease due to the mismatch between visual information and actual body movement. Conversely, the group with low motion sickness sensitivity showed an increasing trend across all indicators. Based on this, it can be interpreted that an individual's innate motion sickness sensitivity has a significant relationship with actual brain response patterns. However, the sample size in this study was small (n=11) and comprised only women, limiting the generalizability of the results. This study confirmed that HMD VR stimulation activates the brain's attentional state. Quantitative EEG analysis suggests that patterns of neurophysiological responses correlate with individual motion sickness susceptibility. Future research should track brain activity changes at the onset of motion sickness and analyze real-time EEG responses to motion sickness triggered by mismatches between visual stimuli and body movement. We emphasize the need for additional studies to expand the sample size.

    Ⅴ. Conclusion

    This study aimed to analyze changes in EEG relative power before and after VR use using dry qEEG equipment, and to identify correlations with individual user characteristics. It was confirmed that VR stimulation activates the brain's attentional state, and that this neurophysiological response correlates with individual motion sickness susceptibility. Analysis of EEG changes revealed distinct results in brain regions responsible for visual stimulation and bodily sensations during VR use. This suggests that VR's visual stimulation affects EEG changes, potentially causing a mismatch between visual input and bodily sensations, which may lead to cybersickness. Future analysis of diverse samples will expand upon these findings, and real-time EEG analysis will enable the exploration of strategies to minimize motion sickness during VR design.

    Conflict of interest

    The authors conclude that they have no interest in the products associated with this study.

    Figure

    Table

    Characteristics of frequency bands in EEG

    SMR*: sensory motor rhythm

    EEG electrodes, Brodmann’s areas and brain functions

    BA; brodmann areas

    VR-Induced Cyber Sickness for Pre-Post Comparison of EEG Relative Power

    Values are presented as median (interquartile range)
    *p<0.050, **p<0.010, ***p<0.001

    Correlation of the EEG changes (ΔEEG) in accordance Motion Sickness Susceptibility

    *p<0.050

    ΔEEG Differences between Low and High MSSQ groups

    Values are presented as median (interquartile range)
    *p<0.050

    Curve Estimation Regression Analysis of EEG changes (ΔRB ch3) and MSSQ

    SE; standard error, S.E.E.; standard error of the estimate

    Curve Estimation Regression Analysis of EEG changes (ΔRMB ch17) and MSSQ

    SE; standard error, S.E.E.; standard error of the estimate

    Curve Estimation Regression Analysis of EEG changes (ΔRHB ch3) and MSSQ

    SE; standard error, S.E.E.; standard error of the estimate

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