fMRI analysis of excessive binocular disparity on the human brain

Kim, Dongchan; Jung, Yong Ju; Han, Youngnam; Choi, Joonsung; Kim, Eunwoo; Jeong, Bumseok; Ro, Yong Man; Park, HyunWook

doi:10.1002/ima.22083

Cited by 14 publications

(9 citation statements)

References 38 publications

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“…In addition, we can also observe that most correlated electrodes are located in the parietal-occipital area. This is also consistent with previous studies, in which parietal-occipital area, especially occipital, play an important role in the 3D visual system [35] and their activity always vary significantly when subjects suffer visual fatigue [18,36,37]. Results indicate that the proposed model indeed has learned frequency-space representations of EEG.…”

Section: Visualizing the Learned Representationsupporting

confidence: 91%

EEG-Based 3D Visual Fatigue Evaluation Using CNN

Kang

Wang

2019

Electronics

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Visual fatigue evaluation plays an important role in applications such as virtual reality since the visual fatigue symptoms always affect the user experience seriously. Existing visual evaluation methods require hand-crafted features for classification, and conduct feature extraction and classification in a separated manner. In this paper, we conduct a designed experiment to collect electroencephalogram (EEG) signals of various visual fatigue levels, and present a multi-scale convolutional neural network (CNN) architecture named MorletInceptionNet to detect visual fatigue using EEG as input, which exploits the spatial-temporal structure of multichannel EEG signals. Our MorletInceptionNet adopts a joint space-time-frequency features extraction scheme in which Morlet wavelet-like kernels are used for time-frequency raw feature extraction and inception architecture are further used to extract multi-scale temporal features. Then, the multi-scale temporal features are concatenated and fed to the fully connected layer for visual fatigue evaluation using classification. In experiment evaluation, we compare our method with five state-of-the-art methods, and the results demonstrate that our model achieve overally the best performance better performance for two widely used evaluation metrics, i.e., classification accuracy and kappa value. Furthermore, we use input-perturbation network-prediction correlation maps to conduct in-depth analysis into the reason why the proposed method outperforms other methods. The results suggest that our model is sensitive to the perturbation of β (14-30 Hz) and γ (30-40 Hz) bands. Furthermore, their spatial patterns are of high correlation with that of the corresponding power spectral densities which are used as evaluation features traditionally. This finding provides evidence of the hypothesis that the proposed model can learn the joint time-frequency-space features to distinguish fatigue levels automatically.2 of 18 ideally indicators. Since mental fatigue is supposed as one aspect of 3D visual fatigue, these features can hardly represent psychological changes [7].Recently, researches have opened up the possibility for visual fatigue assessment with noninvasive approaches based on electroencephalography (EEG) [1]. EEG signals record integration of spontaneous electrical activities of a large number of brain cells from the scalp, which is closely related with mental and physical states [8]. All event-related potentials in EEG are limited in duration and in frequency. The majority of events activate distinct brain regions [9]. This means researchers can extract features from space, time, and frequency dimensions of the EEG data to build efficient classification models for visual fatigue evaluation. Traditionally, the most popular features for visual fatigue evaluation are time-frequency features (e.g., power spectral), which can be obtained by time-frequency transformation (e.g., wavelet transformation). Power spectral of various frequency bands are proven to be related with visual fatigue l...

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Section: Visualizing the Learned Representationsupporting

confidence: 91%

EEG-Based 3D Visual Fatigue Evaluation Using CNN

Kang

Wang

2019

Electronics

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“…Researchers frequently use RDSs to study the neural correlates of stereopsis which can be triggered using binocular disparities. Dynamic RDSs can provide rich stereoscopic stimuli, but cannot completely replace monocular cues such as shade, motion, and occlusions [ 12 ]. In comparison, a static RDS provides depth cues that can be only obtained by binocular disparity, thus making the static RDS an optimal stimulus to study the effect of binocular disparity on visual fatigue.…”

Section: Introductionmentioning

confidence: 99%

Human cortical neural correlates of visual fatigue during binocular depth perception: An fNIRS study

Cai

Zhu

et al. 2017

PLoS ONE

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Functional near-infrared spectroscopy (fNIRS) was adopted to investigate the cortical neural correlates of visual fatigue during binocular depth perception for different disparities (from 0.1° to 1.5°). By using a slow event-related paradigm, the oxyhaemoglobin (HbO) responses to fused binocular stimuli presented by the random-dot stereogram (RDS) were recorded over the whole visual dorsal area. To extract from an HbO curve the characteristics that are correlated with subjective experiences of stereopsis and visual fatigue, we proposed a novel method to fit the time-course HbO curve with various response functions which could reflect various processes of binocular depth perception. Our results indicate that the parietal-occipital cortices are spatially correlated with binocular depth perception and that the process of depth perception includes two steps, associated with generating and sustaining stereovision. Visual fatigue is caused mainly by generating stereovision, while the amplitude of the haemodynamic response corresponding to sustaining stereovision is correlated with stereopsis. Combining statistical parameter analysis and the fitted time-course analysis, fNIRS could be a promising method to study visual fatigue and possibly other multi-process neural bases.

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“…11 More recent studies have used advanced imaging techniques to map brain activity of accommodation and vergence eye movements. [12][13][14][15] Functional magnetic resonance imaging (fMRI) is a non-invasive method that has become a major research tool in studying brain function with use of the blood-oxygenlevel-dependent (BOLD) signal to indicate magnitude and spatial extent of regional neural activity. 1,2 Alvarez, Jaswal, Gohel and Biswal, 2014, used fMRI analysis to compare convergence activation of CI subjects both before and after vision therapy (VT) treatment as well as with normal binocular vision (NBV) controls.…”

Section: Baseline Proceduresmentioning

confidence: 99%

Imaging of Convergence Insufficiency Treatment Effects (ICITE) Pilot Study: Design and Methods

2017

VDR

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Background: The blood-oxygen-level-dependent (BOLD) signal from functional magnetic resonance imaging (fMRI) identifies brain activation during specific tasks.1,2 This paper describes the design and methodology of the Imaging of Convergence Insufficiency Treatment Effects (ICITE) Study, a two-phase study comparing BOLD activations during vergence eye movements in symptomatic convergence insufficiency (CI) subjects (1) to those with normal binocular vision (NBV) and (2) after office-based vergence/accommodative therapy (OBVAT) versus office-based placebo therapy (OBPT). Methods: Young adults, 18 to 30 years, with NBV or symptomatic CI (near exophoria at least 4Δ [prism diopters] greater than at distance, receded near point of convergence [NPC], and insufficient positive fusional vergence [PFV]) were enrolled. During fMRI scanning, subjects were instructed to fuse a random-dot stereogram stimulus with vergence demands ranging from -3Δ to +25Δ. CI subjects were then randomized to 12 weeks of OBVAT or OBPT. Vision and fMRI examination at outcome were performed by a masked examiner. BOLD signal at baseline was compared between NBV and CI patients. Later, the BOLD response at baseline was compared to that following vision therapy for those with CI. Conclusion: The imaging results are expected to advance understanding of neurological mechanisms of CI and the effects of therapy on the vergence system, which in turn may guide development of future research that could lead to new treatment strategies.

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fMRI analysis of excessive binocular disparity on the human brain

Cited by 14 publications

References 38 publications

EEG-Based 3D Visual Fatigue Evaluation Using CNN

EEG-Based 3D Visual Fatigue Evaluation Using CNN

Human cortical neural correlates of visual fatigue during binocular depth perception: An fNIRS study

Imaging of Convergence Insufficiency Treatment Effects (ICITE) Pilot Study: Design and Methods

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