近年來，對於人類大腦中神經活動的研究實屬屢見不鮮，其中以功能性核磁共振造影 (functional Magnetic Resonance Imaging，fMRI) 及腦電圖 (Electroencephalogram，EEG) 為兩大主要的分析檢測儀器。然而，這兩者分別都有各自的優劣之處，於是如何各取其之所長並進行一連串的分析就成為了很有趣的議題。本篇論文中欲達到三大目標：第一，成功整合從兩種儀器中所得的大腦資訊；第二，偵測出進行特定動作時大腦中的反應區域；最後，嘗試去分析這些反應區域之間是否存在著連結關係。
由於fMRI在空間上有相當良好的解析度，於是就利用這種特性在一套名為Statistical Parametric Mapping (SPM) 的腦部醫學軟體上進行反應區域定位的分析，再對反應區域周遭的電極訊號經過轉換運算後得到該區域的EEG訊號，即可利用EEG在時間上有良好解析度的優點來做之後的分析。接著，對訊號施以一連串的磁振干擾消除與雜訊處理還原出真實訊號，並對其進行與動作相關的神經活動反應之驗證，最後，即為本篇論文的核心方法，將訊號透過格蘭傑因果關係模型的計算，推導出在大腦中具有區域性、時間性、方向性與不同頻率區段之間的訊號因果關係。在本篇論文的實驗設計中，受測者會被刺激到的大腦區域有視覺區、前運動區與運動區，我們提出的假設認為這些區域之間必然存在一定程度的關連性，並且透過實驗設計可以彰顯出這樣的神經鏈結；所以我們也就將這些區域定為本研究的焦點區域 (Region of Interest，ROI)。
最後，研究結果隨著各種刺激呈現出相對應的反應區域，並且由於神經訊號在不同的頻率區段中代表著不同的生理意義，於是我們就將EEG訊號分解為主要的六個頻段，隨後在各個頻段中分析訊號間的因果關係。在後續結果的分析統計比較中，不論是區域間的連結關係，或是針對出現因果關係的相應頻段的特性，都得到了顯著且值得探討的結論。

The research of functional neural activity within human brain has been discussed widely in recent years, while functional Magnetic Resonance Imaging (fMRI) and Electroencephalogram (EEG) are the two major equipments for examination. However, they both have their own advantages and weaknesses; therefore, obtaining the advantages and applying a series of analysis would be an interesting subject. There were three goals we wished to achieve: First, the integration of neural information given by two equipments; second, to specify the active brain regions with respect to certain motions; finally, attempting to analyze the functional connectivity between these regions.
Due to the fact that fMRI has high spatial resolution, we utilized it to localize the active regions, also known as regions of Interest (ROIs), through a medical software called Statistical Parametric Mapping (SPM). Then, the EEG signal of ROIs could be obtained by a transformation for combining the EEG signals from nearby electrodes, and thus the advantage of high temporal resolution of EEG could be used in the connectivity analysis. Also, we could restore the true EEG signals back by following a whole series of processing such as artifact elimination and noise reduction; moreover, a data-verification was done to testify the motion-related neural activity. Last was the kernel method of this research, we applied the Granger Causality Model (GCM) to the signals and analyze the causal relation with respect to regions, time, direction, and frequency bands. The experiments were designed to activate the visual areas, premotor area, and motor areas. The hypothesis we proposed was that these regions must have a certain amount of connectivity, and through our experiment design, this neural connectivity was observed evidently. Therefore, these areas were defined as the ROIs of our research.
In summary, the results showed different activated ROI according to different kinds of stimuli, and because of that each frequency band of the neural signal stands for distinct physiological meaning, we decomposed EEG signal into six major frequency bands, then analyzed the causal relation within these bands. We gained some significant conclusions either in the regional connectivity or the characteristic of corresponding frequency bands that revealed the connectivity in the following statistical analysis and comparison which were worthy of discussions.

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