近年來，許多科學家對於人類腦波的分析與研究越來越感興趣，他們利用一種量測腦波的方法，稱作腦電波圖(Electroencephalogram, EEG)去觀測人類的腦波變化，並且建立許多實際的應用，像是遊戲控制以及物理治療上。人腦電腦介面 (Brain computer interfaces, BCIs)可以抓取受測者的腦波訊號，並且傳到後端的系統進行一系列的訊號分析，由此可以對使用者的想法進行推論。
在本篇論文中，我們使用一種新的EEG分析方法，稱作錯誤相關電位(Error-related potential, ErrP)來提升人腦電腦介面的性能，錯誤相關電位是透過受測者對於錯誤的警覺的認知狀態所蒐集到的腦波訊號，它是屬於事件相關電位的一種(Event-related potential, ERP)。並且為了克服人腦電腦介面所擁有的資訊量不確定這項缺點，我們也使用了協同控制策略(shared-control strategy)的概念來提升我們提出的模型健全性。因此我們結合了受測者端的錯誤相關電位以及協同控制策略的概念去提出一個新的模型:繪圖模型(Drawing model)，以及利用我們提出的移動方法:減少與說服搜尋法(Reduce and convince search) (RAC搜尋法)讓系統在接受受測者的反應訊號時，繪圖模型上的物件能根據RAC 搜尋法的規則做移動。並且因為每個受測者訓練出來的分類器不是百分百正確，因此在物件移動過程中，可能會受到分類器誤判的影響導致物件偏離原有的軌道，所以加入了一個機率機制，系統在累積足夠的信心時，就會將偏離軌道的物件退回正常的軌道。我們的目的是希望提出的繪圖模型能夠讓受測者透過自己的腦波操控來順利並且快速地完成object在drawing model上的移動任務，最後我們會建立一系列的實驗，實驗結果顯示我們提出的drawing model比起不加入受測者腦波影響，完全無背景知識的try and error模型來說，整體效能提升了約16%

Many scientists are interested in the analysis of human’s brainwave using electroencephalography (EEG) and conducting several applications such as game-controlling and physical therapy based on the EEG analysis. A brain-computer interface (BCI) could capture subjects’ EEG signals and to some extent infer the subject’s intention by analyzing the signals in a back-end system.
In my thesis, we propose to use a novel kind of EEG analysis that is called error-related potential (ErrP) to enhance the performance and applicability of BCIs. ErrP is based on the fact that human’s cognitive state can be aware of error, and the unique kind of brainwave will be produced to reflect this cognitive state called ErrP which belongs to a kind of event-related potential (ERP). For conquering the low information rate of brain-computer interface, we use the shared-control strategy to enhance the robustness of our proposed model. Therefore, we combine the ErrP from human subject and shared-control strategy to propose a new kind model: Drawing model, and use the moving method : Convince and Reduce search (RAC search) we proposed to make the object move on the drawing model according to the rule of RAC search as system receive the response EEG signal of human subject.
Because the accuracy of each subject’s personal trained classifier is not 100%, therefore, the object might deviate from the normal track while the classifier make a misclassification. So we add a probability mechanism for backtracking the out-of-track object to the normal track as the system has enough confidence. Our objective is to make the object complete the drawing mission quickly by using our drawing model. In the last, we will conduct a series of analysis for our experimental results, and the results show that our proposed drawing model enhance about 16% performance by comparing to the try and error model which is without the influence of subjects’ brainwave.

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