使用口語是最快速且直接的溝通方式。在對話的過程中，人會經歷許多認知歷程，其中包含語音的知覺、語言的產出等等。如果任何一個與對話相關的認知歷程在過程中出現問題，則此人可能無法順利表達自己的思想；並且在嚴重時，可能會被診斷為溝通障礙。然，溝通障礙可以根據不同的認知歷程障礙分為不同的亞型，並且障礙的嚴重程度也可以被劃分為輕微到嚴重。此外，不同的溝通障礙亞型所需之治療以及幫助也是不同的。在此博士論文，我將針對兩種溝通障礙族群(聽力障礙以及語言障礙)的需求，結合功能性磁振造影(紀錄神經活動反應)，反向推理的實驗策略，以及機器學習之算法，開發相對的應用工具。以下我將簡述此二應用工具之目標以及背景。(1) 部分但不是所有聽力障礙患者在經歷治療後（例如:植入人工耳蝸後），都可以一定程度上的恢復聽力。此外，術後康復情形也有不一的現象。因此，我嘗試使用大腦活動反應建立一個生物標誌預測和評估術後結果。 (2) 相反的，多數語言障礙的族群並沒有辦法藉由手術改善其所面臨之問題。對此，我使用開創性的手法，開發新的神經語言解碼策略，期待促進神經科學在醫學的應用。

Speech communication is our firsthand, fast, and foremost way to exchange information. It comprises various types of cognitive processing, including sound perception, speech production, verbal working memory, and so on. If there is any problem in the relevant cognitive processing, one might fail to convey him/herself and in some serious cases be diagnosed with a communication disorder. Communication disorders range from mild to severe and can be categorized into different subtypes based on distinct cognitive processing impairments. Besides, distinct subgroups require varied treatments and assistants. In the present work, I targeted specific demands of the two main subgroups of communication disorders, i.e., hearing disorder and speech disorder. (1) Some but not all patients with hearing disorders can regain hearing abilities to some extent (e.g., via cochlear implant), but they showed varied degrees of rehabilitation, sometimes longer than six months. Therefore, a biomarker is required for predicting and evaluating the outcomes. (2) Moreover, the speech disorders patients whose communication problems cannot be solved by surgery require new technologies to help convey their thoughts. I demonstrated that these two needs (i.e., rehabilitation biomarker and thought decoder) can be fulfilled by combing the neuron activity signals acquired by functional Magnetic Resonance Imaging (fMRI) with reverse inference (i.e., inferring the engagement of cognition processes given the brain activation) and machine learning algorithms.

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