The assessment of the visual discomfort caused by vergence‐accommodation conflicts based on EEG

Abstract

Stereoscopic displays have gained much popularity, while visual discomfort during watching stereoscopic displays is becoming an increasing concern. Vergence‐accommodation conflicts are one of factors inducing visual discomfort. How to properly assess the visual discomfort by vergence‐accommodation conflicts has been the focus of researchers. Compared with previous studies, this paper aims to address two problems: (1) designing an elaborate experiment to induce visual discomfort without interference of the long performance time and (2) objectively selecting fewer electrodes and extracting electroencephalogram (EEG) features much related to visual discomfort. The results show that, better than previous experimental paradigm, the designed experimental paradigm not only causes the visual discomfort properly but also shortens the performance time to less than an hour. Moreover, for the proposed model, the visual discomfort can be evaluated well based only on six selected electrodes or even better than previous models relying on much more electrodes.

Mixture Kernel Density Estimation and Remedied Correlation Matrix on the EEG-Based Copula Model for the Assessment of Visual Discomfort

“…The rounded average level from all participants is regarded as a label of the stimulus. Referring to the studies of other researchers and our previous research [5,[40][41][42], disparity images with disparities ranging from ±0.1 • to ±1 • are used as stimuli. The designed stimuli are presented by the LG D2343P display with 1920 × 1080 resolution.…”

3D Visual Discomfort Assessment With a Weakly Supervised Graph Convolution Neural Network Based on Inaccurately Labeled EEG

“…Accurately assessing the degree of visual discomfort is crucial for developing effective intervention measures and improving user experience [1], [2]. Electroencephalography (EEG) has emerged as a potent tool for objectively assessing visual discomfort [3]- [5]. Its high temporal resolution and rich information content enable EEG signals to capture real-time brain states, offering valuable insights for visual discomfort assessment.…”

3‐3: Invited Paper: Prediction Model for Visual Fatigue Caused by Smartphone Display Based on EEG Multi‐dimensional Features

“…In Wiyor et al's study [9], a feed-forward artificial neural network (FF-ANN) classifier was made to learn four visual fatigue states from EEG frequential features. In Zheng et al's study [10], a visual discomfort classifier was established by support vector machine (SVM), and EEG features related to the relative energy were selected as input. Until now, the applications in visual fatigue prediction were limited, and there is room for improvement in performance.…”