Study of Cybersickness Prediction in Real Time Using Eye Tracking Data

Shimada, Shogo; Ikei, Yasushi; Nishiuchi, Nobuyuki; Yem, Vibol

doi:10.1109/vrw58643.2023.00278

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…Currently, researchers have focused on VIMS detection using physiological indicators such as EEG, eye movement, and heart rate [8][9][10][11][12][13]. Among these indicators, EEG signals have garnered significant attention due to their rich data and information content.…”

Section: Introductionmentioning

confidence: 99%

Classification of Visually Induced Motion Sickness Based on Phase-Locked Value Functional Connectivity Matrix and CNN-LSTM

Shen,

Liu,

et al. 2024

Sensors

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To effectively detect motion sickness induced by virtual reality environments, we developed a classification model specifically designed for visually induced motion sickness, employing a phase-locked value (PLV) functional connectivity matrix and a CNN-LSTM architecture. This model addresses the shortcomings of traditional machine learning algorithms, particularly their limited capability in handling nonlinear data. We constructed PLV-based functional connectivity matrices and network topology maps across six different frequency bands using EEG data from 25 participants. Our analysis indicated that visually induced motion sickness significantly alters the synchronization patterns in the EEG, especially affecting the frontal and temporal lobes. The functional connectivity matrix served as the input for our CNN-LSTM model, which was used to classify states of visually induced motion sickness. The model demonstrated superior performance over other methods, achieving the highest classification accuracy in the gamma frequency band. Specifically, it reached a maximum average accuracy of 99.56% in binary classification and 86.94% in ternary classification. These results underscore the model’s enhanced classification effectiveness and stability, making it a valuable tool for aiding in the diagnosis of motion sickness.

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