近年來，有關腦科學研究可說是當代顯學，目前由於生物、醫學、物理與電機資訊工程的整合之下，使得相關研究與應用均大幅進步。譬如睡眠狀態研究，有關腦波狀態，皮質興奮區域等研究，都有突破性進展。然而，大部分這樣的睡眠量測，多數為侵襲性測量，對睡眠本身會有或多或少的干擾。是否有一種較為低廉，非接觸式的量測方式且能得到相對應的生理訊號？
綜觀生理訊號，大多為μV左右甚至更弱，加上屏蔽不易，很難量測。而眼電訊號(EOG)訊號不弱(mV級)，且與睡眠狀態有關。若能以遙測方式取得EOG訊號，則可以得到一種非侵襲性，價廉的睡眠監測方法，因此本論文著重於能夠利用電腦視覺的方法，找出與目前傳統眼電圖（EOG）所量測到的眼電訊號有關聯對應的眼睛移動影像特徵。希望藉由眼電圖眼電訊號與電腦視覺特徵之間的關聯性，未來可以應用於開發電腦視覺的眼電圖及相關眼電應用上。在影像前置處理步驟上，我們會利用睫毛的位置，進一步定位眼睛可能移動的範圍，此範圍稱之為ROI。我們再將ROI做特徵抽取，分別抽取四種特徵：空間特徵(Spatial Domain Features)、統計特徵(Statistical Features)、頻率特徵(Frequency Domain Features)、熵特徵(Entropy Feature)。並將四種特徵與實際量測的眼電訊號比對，經過比對，我們可以發現熵特徵與眼電訊號有很好的對應。因此熵特徵對於發展電腦視覺方式的眼電圖將會是一個很好的關聯對應。

The brain scientific research can be regarded as one of the contemporary popular studies in recent years. The integration of biology, medicine, physics, electrical and information engineering has resulted in a substantial development in brain related researches and applications. For example, there were breakthrough progresses in the researches on sleeping status, brain waves status, and excitatory zone of cortex, etc. In sleep studies, the majority of sleep measurements are conducted by using invasive sensing approaches which will more or less disturb the sleep. It’s natural to ask whether there exists a noninvasive approach that is not only cheaper and non-contact sensing, but also able to obtain the corresponding physiological signal.
By looking at the physiological signals comprehensively, we discovered that most values of theirs strength are in μV or weaker if in a form of voltage signal; in addition, they are even weaker and difficult to measure if in the magnetic field signal form because of the difficulty of screening. However, the signal of Electrooculogram (EOG), with its stronger signal strength (in mV level), and related to the sleeping status, is frequently adopted along with other physiological measurements in the sleep study. If it is possible to use the remote sensing technique to acquire the EOG signal, a non-invasive and cheap approach of monitoring sleep may be obtained then. Therefore, this study is emphasized on the possibility of using the computer vision method to establish the function of EOG signal obtained from the traditional electrode. In order to develop the computer vision EOG, we have to seek out the correlation between the EOG and features of eye images obtained form computer vision. We thus utilized the digital image processing techniques to find out the image features of eye movement under close eye condition that related to the EOG. In pre-processing stage, we determined the position of eyelashes by examining the images from the video sequences taken of the close eye, and further to position the moveable range for eyes, named as the ROI (range of image). Then, we conducted the process of feature extraction to extract out 4 features: Spatial Domain Feature, Statistical Feature, Frequency Domain Feature, and Entropy Feature, respectively. Next, we investigate their correlations to the EOG by comparing these 4 features with the EOG signals obtained from the actual EOG measuring process. We then discovered a good correspondence between the Entropy Feature and EOG signal. As a result, the Entropy Feature may be a better approach of correspondence to develop the computer vision EOG.

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