Dual-pathway EEG model with channel attention for virtual reality motion sickness detection

Abstract

Background

Motion sickness has been a key factor affecting user experience in Virtual Reality (VR) and limiting the development of the VR industry. Accurate detection of Virtual Reality Motion Sickness (VRMS) is a prerequisite for solving the problem.

New method

In this paper, a dual-pathway model with channel attention for detecting VRMS is proposed. The proposed model has two pathways that both consist of CNN blocks and channel attention modules. The first pathway takes the EEG signal as inputs. The second pathway transforms the EEG signal into brain networks of six frequency bands using Phase Locking Value (PLV) or ρ index (RHO) methods and takes the adjacent matrixes as input. The features from the two pathways are connected and fed into the fully connected layer for classification. Finally, a VR flight simulation experiment is performed and the EEG of the resting state before and after the virtual flight task are collected to validate the model.

Results

The average accuracy, precision, recall, and F1 score of the proposed model are 99.12 %, 99.12 %, 99.11 %, and 99.12 %, respectively.

Comparison with existing methods

Eight models are introduced as the reference methods and four of them are fused as the hybrid models in this study. The results show that the proposed model is better than those state-of-art models.

Conclusions

The proposed model outperforms the state-of-the-art models and provides objective and direct guidance for overcoming VRMS and optimizing VR experience.