以腦波特徵評估不同螢幕尺寸與視覺呈現方式對視覺疲勞之影響

Exploring the Effect of Different Screen Sizes and Visual Presentation on Visual Fatigue Based on EEG

蕭盟興(Meng-Hsing Hsiao)

指導教授 ： 姜琇森

國立臺中科技大學/資訊與流通學院/資訊管理系碩士班/碩士(2017年)

摘要

隨著資訊科技的進步與環境快速變化，多數人長時間注視電腦、手機和平板，每日用眼負荷量也隨著提高。然而行動網路的快速發展，也帶來一些負面的影響，在網路帶來更多不同的應用與便利的同時，但是無形中人們往往長時間的使用電腦與行動裝置，使得對眼睛造成的傷害，這是目前相當嚴重的問題。
近幾年隨著顯示技術的進步，人們不再只是侷限在2D顯示技術上，隨著3D顯示技術的研究，以及3D電影的推出，近幾年有更多呈現方式也慢慢融入人們的生活應用中，像是擴增實境(Augmented Reality, AR)或是虛擬實境(Virtual Reality, VR)。這些顯示技術對於眼睛與視覺造成的影響，還沒有明確的定論，只有3D顯像技術會對眼睛造成負擔，容易產生視覺疲勞。過去從事視覺疲勞檢測的研究中，針對3D進行的研究也較少加入其他共通因素(螢幕大小與視覺呈現)，且也較無將擴增實境(AR)或是虛擬實境(VR)的內入一起探討視覺疲勞，更甚少研究透過生理參數分析進而將視覺疲勞量化。而腦電圖(Electroencephalogram, EEG)是一項重要的生理參數，記錄大腦的電波變化，透過腦波特徵的量測可觀察大腦因視覺疲勞發生的變化，腦電圖已被運用在視覺疲勞的研究，是一個客觀的視覺疲勞評估工具。
本研究使用線性判別分析(Linear Discriminant Analysis, LDA)與最小-最大縮放法(Min-Max scaling)建立視覺疲勞評估模型，並用於探討不同螢幕大小與視覺呈現方式對視覺疲勞之影響。再者，本研究設計一個螢幕大小(手機、平板、電腦) × 視覺呈現方式(2D、3D、AR、VR)的實驗情境，去觀察不同螢幕大小的受測者在不同視覺呈現方式下的視覺疲勞程度。本研究也透過分析8個極點的腦波數據找出在不同視覺呈現方式下(2D, 3D, AR, VR)影響視覺疲勞的關鍵腦波特徵。
依據本研究的發現：(1)影響2D與3D之腦波特徵在前額葉Fp1與Fp2極點的Delta與Theta波，AR在枕葉O1與O2極點的Alpha波，而VR在左中央區C3極點的Delta與Theta波；(2)從螢幕大小來看，發現螢幕越大則視覺疲勞越大，表示當同一時間的視覺刺激量過多，會導致視覺負荷的增加，並造成更多視覺疲勞的發生；(3)結果表明VR會造成相當嚴重的視覺疲勞，而從VR之特性來看，主要是因感覺不匹配所導致的暈動症(motion sickness)，故建議避免大腦與身體感受不一致之用眼行為，如走路滑手機、坐車看書等。藉由本研究提出之視覺疲勞評估模型，使得視覺疲勞檢測容易，且有客觀的量化指標，透過警示視覺疲勞的發生，以避免眼睛健康遭受危害。

關鍵字

視覺疲勞 ； 腦電圖 ； 線性判別分析 ； 視覺呈現 ； 三維 ； 虛擬實境

並列摘要

With the rapid changes of information technology, Most people spend more and more time in front of screens of computers, cell phones and tablets but could this be causing damage to your eyes? Although the Internet brings more different applications and convenience, people tend to use computers and mobile devices for a long time, making the damage to the eyes. It will be a very serious problem.
In recent years, display technology has advanced significantly and it is no longer limited to only two-dimensional (2D) flat images. With rapid advances in the optics, and projection technologies, three-dimensional (3D), augmented reality (AR) and virtual reality (VR) display technologies are making their way into the marketplace. However, most people are oblivious to understand the effects of these display technologies (especially AR and VR) use on the eyes and vision, which has not clear conclusion. Some researches show that watching 3D imaging technology will increase the burden on the eye, and cause visual fatigue and discomfort. In the past, few studies add AR and VR visual presentation types to investigate the effects of the display type, screen size on visual fatigue and discomfort. Moreover, fewer studies also try to quantify visual fatigue by analyzing physiological parameters. Researches find that electroencephalogram (EEG) not only can be used to observe changes in the brain, but also become an objective visual fatigue assessment tool.
This study develops an EEG-based applicable model to detect and quantify visual fatigue by using linear discriminant analysis and min-max scaling. This study also designs an experiment: screen sizes (phone, tablets, computer,) × visual presentation (2D, 3D, AR, VR) to observe the effects of different screen sizes and use of different imaging technologies on visual fatigue. The proposed assessment model is used to explore the effects of different screen sizes and visual presentation on visual fatigue.
The results show that the key brain wave features respond visual fatigue on 2D, 3D visual presentation are the delta and theta in the prefrontal lobe (Fp1 and Fp2). The key brain waves that reflect the visual fatigue on AR and VR visual presentation are the alpha in in the temporal (O1 and O2), and the delta and theta in the left central sulcus, respectively. In addition, we find out that the visual fatigue will be increased with a greater screen size. Moreover, VR can cause considerable visual fatigue in all visual presentation types, even greater than the 3D.
Our proposed model will provide references for the individual's visual fatigue state. The research results will to help people to avoid hurting eyes when watching digital screens and keep their eyes healthy.

並列關鍵字

Visual fatigue ； EEG ； Linear discriminant analysis ； Visual presentation ； 3D ； Virtual reality

參考文獻

Bando, T., Iijima, A., & Yano, S. (2012). Visual fatigue caused by stereoscopic images and the search for the requirement to prevent them: A review. Displays, 33(2), 76-83.

Belyavin, A., & Wright, N. A. (1987). Changes in electrical activity of the brain with vigilance. Electroencephalography and clinical Neurophysiology, 66(2), 137-144.

Benedetto, S., Carbone, A., Drai-Zerbib, V., Pedrotti, M., & Baccino, T. (2014). Effects of luminance and illuminance on visual fatigue and arousal during digital reading. Computers in Human Behavior, 41, 112-119.

Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2-3), 191-203.

Blinowska, K. J., Rakowski, F., Kaminski, M., Fallani, F. D. V., Del Percio, C., Lizio, R., & Babiloni, C. (2017). Functional and effective brain connectivity for discrimination between Alzheimer’s patients and healthy individuals: A study on resting state EEG rhythms. Clinical Neurophysiology, 128(4), 667-680.