Wavelet Transform and Attentional Dual-Path EEG Model for Virtual Reality Motion Sickness Detection

CHEN Yuechi; HUA Chengcheng; DAI Zhian; FU Jingqi; ZHU Min; WANG Qiuyu; YAN Ying; LIU Jia

doi:10.11999/JEIT251233

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CHEN Yuechi, HUA Chengcheng, DAI Zhian, FU Jingqi, ZHU Min, WANG Qiuyu, YAN Ying, LIU Jia. Wavelet Transform and Attentional Dual-Path EEG Model for Virtual Reality Motion Sickness Detection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251233

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CHEN Yuechi, HUA Chengcheng, DAI Zhian, FU Jingqi, ZHU Min, WANG Qiuyu, YAN Ying, LIU Jia. Wavelet Transform and Attentional Dual-Path EEG Model for Virtual Reality Motion Sickness Detection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251233

Citation:

CHEN Yuechi, HUA Chengcheng, DAI Zhian, FU Jingqi, ZHU Min, WANG Qiuyu, YAN Ying, LIU Jia. Wavelet Transform and Attentional Dual-Path EEG Model for Virtual Reality Motion Sickness Detection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251233

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Wavelet Transform and Attentional Dual-Path EEG Model for Virtual Reality Motion Sickness Detection

doi: 10.11999/JEIT251233 cstr: 32379.14.JEIT251233

CHEN Yuechi¹,
HUA Chengcheng^{1, 2
,
,},
DAI Zhian¹,
FU Jingqi¹,
ZHU Min¹,
WANG Qiuyu¹,
YAN Ying^{1, 2},
LIU Jia^{1, 2}

1.
Jiangsu Province Engineering Research Center of Intelligent Meteorological Exploration Robot, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China

Funds: The National Natural Science Foundation of China (62206130), The Natural Science Foundation of Jiangsu Province (BK20200821), The Startup Foundation for Introducing Talent of NUIST (2020r075)

Received Date: 2025-11-24
Accepted Date: 2026-01-12
Rev Recd Date: 2026-01-10

Available Online: 2026-01-24

Abstract

Abstract

Objective Virtual Reality Motion Sickness (VRMS) presents a barrier to the wider adoption of immersive Virtual Reality (VR). It is primarily caused by sensory conflict between the vestibular and visual systems. Existing assessments rely on subjective reports that disrupt immersion and do not provide real-time measurements. An objective detection method is therefore needed. This study proposes a dual-path fusion model, the Wavelet Transform ATtentional Network (WTATNet), which integrates wavelet transform and attention mechanisms. WTATNet is designed to classify resting-state ElectroEncephaloGraph (EEG) signals collected before and after VR motion stimulus exposure to support VRMS detection and research on the mechanisms and mitigation strategies. Methods WTATNet contains two parallel pathways for EEG feature extraction. The first applies a Two-Dimensional Discrete Wavelet Transform (2D-DWT) to both the time and electrode dimensions of the EEG, reshaping the signal into a two-dimensional matrix based on the spatial layout of the scalp electrodes in horizontal or vertical form. This decomposition captures multi-scale spatiotemporal features, which are then processed using Convolutional Neural Network (CNN) layers. The second pathway applies a one-dimensional CNN for initial filtering followed by a dual-attention structure consisting of a channel attention module and an electrode attention module. These modules recalibrate the importance of features across channels and electrodes to emphasize task-relevant information. Features from both pathways are fused and passed through fully connected layers to classify EEGs into pre-exposure (non-VRMS) and post-exposure (VRMS) states based on subjective questionnaire validation. EEG data were collected from 22 subjects exposed to VRMS using the game “Ultrawings2.” Ten-fold cross-validation was used for training and evaluation with accuracy, precision, recall, and F1-score as metrics. Results and Discussions WTATNet achieved high VRMS-related EEG classification performance, with an average accuracy of 98.39%, F1-score of 98.39%, precision of 98.38%, and recall of 98.40%. It outperformed classical and state-of-the-art EEG models, including ShallowConvNet, EEGNet, Conformer, and FBCNet (Table 2). Ablation experiments (Tables 3 and 4) showed that removing the wavelet transform path, the electrode attention module, or the channel attention module reduced accuracy by 1.78%, 1.36%, and 1.01%, respectively. The 2D-DWT performed better than the one-dimensional DWT, supporting the value of joint spatiotemporal analysis. Experiments with randomized electrode ordering (Table 5) produced lower accuracy than spatially coherent layouts, indicating that 2D-DWT leverages inherent spatial correlations among electrodes. Feature visualizations using t-SNE (Figures 5 and 6) showed that WTATNet produced more discriminative features than baseline and ablated variants. Conclusions The dual-path WTATNet model integrates wavelet transform and attention mechanisms to achieve accurate VRMS detection using resting-state EEG. Its design combines interpretable, multi-scale spatiotemporal features from 2D-DWT with adaptive channel-level and electrode-level weighting. The experimental results confirm state-of-the-art performance and show that WTATNet offers an objective, robust, and non-intrusive VRMS detection method. It provides a technical foundation for studies on VRMS neural mechanisms and countermeasure development. WTATNet also shows potential for generalization to other EEG decoding tasks in neuroscience and clinical research.

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References(37)

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