VR motion sickness recognition by using EEG rhythm energy ratio based on wavelet packet transform

Li, Xiaolu; Xu, Cangsu; Zhu, Junjiang; Li, Yuntang; Wu, Shiqian

doi:10.1016/j.cmpb.2019.105266

Cited by 38 publications

(26 citation statements)

References 30 publications

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“…There appears to be a continuous and gradual onset in which subjects and patients cannot describe exactly what is wrong at the beginning but soon develop a feeling of vertigo and nausea. Previous studies investigating the neurophysiological basis underlying this phenomenon found fluctuations of EEG rhythms especially in lower frequencies while experiencing VIMS (Chelen et al, 1993;Kim et al, 2005;Chen et al, 2010;Arsalan Naqvi et al, 2015;Chuang et al, 2016;Li et al, 2020). Krokos and Varshney (2021) as well as Lim et al (2021) were recently able to show an increase of Delta (1-3 Hz), Theta (4-7 Hz) and Alpha (8-13 Hz) power in VIMS.…”

Section: Introductionmentioning

confidence: 91%

“…Virtual reality motion sickness induced changes upon the frequency powers in EEG are commonly reported (Min et al, 2004;Kim et al, 2005;Jensen and Colgin, 2007;Lin et al, 2007;Park et al, 2008;Chen et al, 2010;Chuang et al, 2016;Li et al, 2020;Krokos and Varshney, 2021). Conversely, the exact patterns of these changes are inconsistent and open to debate.…”

Section: Eeg In Virtual Reality Induced Motion Sicknessmentioning

confidence: 97%

“…Modern neuroimaging methods are in principle well suited to study changes in brain function. However, since VIMS involves body movements in a virtual simulation environment and potential risks include vomiting and drowsiness, EEG studies are preferred for this topic (Kim et al, 2005;Lin et al, 2007Lin et al, , 2013Li et al, 2020;Lim et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mismatch of Visual-Vestibular Information in Virtual Reality: Is Motion Sickness Part of the Brains Attempt to Reduce the Prediction Error?

Nürnberger

Klingner

Witte

et al. 2021

Front. Hum. Neurosci.

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Visually induced motion sickness (VIMS) is a relevant limiting factor in the use of virtual reality (VR) devices. Understanding the origin of this problem might help to develop strategies to circumvent this limitation. Previous studies have attributed VIMS to a mismatch between visual, and vestibular information, causing ambiguity of the position of the body in relation to its surrounding. Studies using EEG have shown a shift of the power spectrum to lower frequencies while VIMS is experienced. However, little is known about the relationship between the intensity of the VIMS and the changes in these power spectra. Moreover, the effect of different varieties of VIMS on the causal relationship between brain areas is largely unknown. Here, we used EEG to study 14 healthy subjects in a VR environment who were exposed to increasing levels of mismatch between vestibular and visual information. The frequency power and the bivariate transfer entropy as a measure for the information transfer were calculated. We found a direct association between increasing mismatch levels and subjective VIMS. With increasing VIMS, the proportion of slow EEG waves (especially 1–10 Hz) increases, especially in temporo-occipital regions. Furthermore, we found a general decrease in the information flow in most brain areas but especially in brain areas involved in the processing of vestibular signals and the detection of self-motion. We hypothesize that the general shift of frequency power and the decrease in information flow while experiencing high intensity VIMS represent a brain state of a reduced ability to receive, transmit and process information. We further hypothesize that the mechanism of reduced information flow is a general reaction of the brain to an unresolvable mismatch of information. This reaction aims on transforming a currently unstable model with a high prediction error into a stable model in an environment of minimal contradictory information.

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Section: Introductionmentioning

confidence: 91%

Section: Eeg In Virtual Reality Induced Motion Sicknessmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mismatch of Visual-Vestibular Information in Virtual Reality: Is Motion Sickness Part of the Brains Attempt to Reduce the Prediction Error?

Nürnberger

Klingner

Witte

et al. 2021

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

“…Therefore, by adding other sensory stimuli in Study II, the participants could temporarily get away from reality and forget their worries, through experiencing environmental content and interactive activities, thus enhancing their immersion, relaxation, and emotional relief. In addition, most participants were first-time users of VR devices and were not comfortable with the weight of the headset, handle operation, or vertigo caused by the brain's asynchronous perception of movement [114,115], which affected the overall experience, to a certain extent; in future research, this should be taken into consideration, and timely attention should be paid to the physical and mental state of the participants. In addition, we provided participants with a quiet urban environment without people and discovered the healing effect of VR urban scenes.…”

Section: Healing Effect Of a Vr Restorative Environmentmentioning

confidence: 99%

Effect of a Virtual Reality-Based Restorative Environment on the Emotional and Cognitive Recovery of Individuals with Mild-to-Moderate Anxiety and Depression

Dong

Wang

et al. 2021

IJERPH

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In this study, restorative environment theory and virtual reality (VR) technology were combined to build different 3D dynamic VR interactive scenes. We discuss the effects of a VR restorative environment on the emotional and cognitive recovery of individuals with mild-to-moderate anxiety and depression. First, we built a VR restorative garden scene, divided into four areas: forest, lawn, horticultural planting, and water features. The scene was verified to have a good recovery effect in 26 participants. Then, 195 participants with mild-to-moderate anxiety and depression were selected as experimental subjects. Through psychological testing and EMG (Electromyography) and EEG (Electroencephalography) data feedback, we further explored the differences in the sense of presence in VR restorative scenes and their effect on individual emotional and cognitive recovery. The results showed that (1) both the restorative environment images and the VR scenes had a healing effect (the reduction in negative emotions and the recovery of positive emotions and cognition), with no difference in the subjective feeling of recovery among the different scenes, but the recovery score of the VR urban environment was higher than that of the natural environment (differing from the results in real environments); (2) a high sense of presence can be experienced in different VR scenes, and interactive activities in VR scenes can provide a great presence experience; (3) the recovery effects of VR restorative environment on emotion and self-efficacy are realized through the presence of VR scenes; (4) a VR restorative environment is helpful for the emotional improvement and cognitive recovery of individuals with mild-to-moderate anxiety and depression. VR urban scenes also have good recovery effects. In terms of cognitive recovery, self-efficacy improved significantly. In addition, from the perspective of EEG indicators, the VR restorative scene experience activated the prefrontal lobe, which is conducive to cognitive recovery in individuals with mild-to-moderate anxiety and depression. In terms of emotional improvement, negative emotions were significantly reduced in the different VR scene groups. In conclusion, we further explored ways to help individuals with mild-to-moderate anxiety and depression, in order to promote the development and application of mental health.

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“…By applying a stepwise transformation from the time domain to the frequency domain, wavelet packet transform (WPT) provides the possibility of in‐depth analysis for nonstationary signals. During the repeated filtering operation, the signal resolution decreases in time domain while the signal resolution increases in frequency domain 38 . WPT can provide information on the time and frequency of EEG signals.…”

Section: The Proposed Methods (Named 3dcner)mentioning

confidence: 99%

Three‐dimensional feature maps and convolutional neural network‐based emotion recognition

Zheng

Yin

et al. 2021

Int J of Intelligent Sys

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In recent years, automatic emotion recognition renders human–computer interaction systems intelligent and friendly. Emotion recognition based on electroencephalogram (EEG) has received widespread attention and many research results have emerged, but how to establish an integrated temporal and spatial feature fusion and classification method with improved convolutional neural networks (CNNs) and how to utilize the spatial information of different electrode channels to improve the accuracy of emotion recognition in the deep learning are two important challenges. This paper proposes an emotion recognition method based on three‐dimensional (3D) feature maps and CNNs. First, EEG data are calibrated with 3 s baseline data and divided into segments with 6 s time window, and then the wavelet energy ratio, wavelet entropy of five rhythms, and approximate entropy are extracted from each segment. Second, the extracted features are arranged according to EEG channel mapping positions, and then each segment is converted into a 3D feature map, which is used to simulate the relative position of electrode channels on the scalp and provides spatial information for emotion recognition. Finally, a CNN framework is designed to learn local connections among electrode channels from 3D feature maps and to improve the accuracy of emotion recognition. The experiments on data set for emotion analysis using physiological signals data set were conducted and the average classification accuracy of 93.61% and 94.04% for valence and arousal was attained in subject‐dependent experiments while 83.83% and 84.53% in subject‐independent experiments. The experimental results demonstrate that the proposed method has better classification accuracy than the state‐of‐the‐art methods.

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VR motion sickness recognition by using EEG rhythm energy ratio based on wavelet packet transform

Cited by 38 publications

References 30 publications

Mismatch of Visual-Vestibular Information in Virtual Reality: Is Motion Sickness Part of the Brains Attempt to Reduce the Prediction Error?

Mismatch of Visual-Vestibular Information in Virtual Reality: Is Motion Sickness Part of the Brains Attempt to Reduce the Prediction Error?

Effect of a Virtual Reality-Based Restorative Environment on the Emotional and Cognitive Recovery of Individuals with Mild-to-Moderate Anxiety and Depression

Three‐dimensional feature maps and convolutional neural network‐based emotion recognition

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