根據研究，人腦經過想像訓練後，其大腦結構會有重組(reorganization)之現象。故本研究使用多通道腦波(multi-channel electroencephalography)透過數位訊號處理進行大腦聯結之識別與分析，探討大腦聯結結構的變化，並藉此彌補功能性磁振照影(functional magnetic resonance imaging，fMRI)時間解析度不佳與成本昂貴之缺點。
本研究共招募1位中風病患與2位常人，並使用實驗室前人發展的腦機介面(brain-computer-interface，BCI)控制復健機器人之復健系統，進行中風病患與常人受測者的腦部想像訓練。最後，將訓練過程中所擷取的多通道腦波，藉由相位斜率指標(phase-slope index，PSI)，分析電極間之聯結與因果關係，建立大腦聯結結構，更透過R-square分析腦波能量變動與想像動作的相關性，評估大腦於想像時的活化區域，並進行受測者訓練前後大腦結構差異之分析與評估，以觀察大腦重組之現象。
研究結果顯示，大腦聯結結構具有動態特性，並且於聯結結構與R-square的分析中，可以驗證大腦能藉由學習產生結構重組。

According to previous research, researchers believe that brain could gradually learn to modulate its brain-connectivity structure in the motor imagery training. The common method to measure the brain-network is often by using functional magnetic resonance imaging (fMRI). However, the drawbacks of fMRI are high cost and low temporal resolution. On the contrary, EEG has the advantages of low cost and high temporal resolution and may be employed to overcome the drawbacks of fMRI. Therefore, the aim of this thesis is to explore and analyze the plasticity of brain using multi-channel electroencephalogram system.
One stroke patient and two healthy subjects participated in this study using a brain-computer-interface (BCI) controlled robot system which has been developed in our previous study as a platform to train subjects to control 1-D cursor movement and orthotics through their motor imagery. Then an EEG-based brain network was constructed from the multi-channel EEG recorded during the BCI training. In addition, R-square originated from linear regression was employed to compute the correlation between EEG power variation and the motor imagery and to indicate the active region of brain stimulated from motor imagery. The quantitative analysis of the change of the brain was conducted through statistical analyses to explore the plasticity of brain. The results of this study showed the response of the brain-connectivity to rest and motor imagery is dynamic and brain could learn to reorganize itself during the BCI training.

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