生理訊號量測在生物醫療研究以及臨床領域中都扮演重要角色。本論文提出兩組與生理訊號量測相關的嵌入式系統，兩組系統都是基於微控制器與無線通訊技術。
第一組系統為無線模組化多重睡眠電圖系統。多重睡眠電圖系統在使用者睡眠時持續同步紀錄多個生理訊號。此系統常在醫院或睡眠中心被用來診斷睡眠障礙，然而在傳統的多重睡眠電圖系統中過多的連接線常會干擾睡眠品質。本論文提出的無線多重睡眠電圖系統由多個小型、低價且使用無線同步的訊號收集節點組成，每組節點收集小部分身體區域之特定生理訊號，藉此降低訊號纜線造成的睡眠障礙。為了驗證此系統的準確度，我們將此系統與另一市售的多重睡眠電圖系統同時設置於使用者身上進行整夜的生理訊號收錄，並且比較兩組系統收錄的訊號。實驗結果顯示，除了與市售之多重睡眠電圖有大於93%的訊號一致性之外，由於降低了訊號纜線造成的睡眠干擾，就多個主觀與客觀的睡眠品質指標而言，使用者於使用模組化多重睡眠電圖系統時有較好的睡眠舒適度。
本論文提出的第二組系統為一組無線行動神經回饋訓練系統。神經回饋訓練近年來常被用於提升認知功能或是改善臨床症狀。現有的神經回饋系統大多為了實驗室環境所設計，利用纜線連接使用者與訓練裝置，使用者在進行訓練時並不方便。本論文提出的無線神經回饋系統由一組腦電圖分析裝置與智慧型手機透過低功耗藍牙無線技術連結而組成。我們透過一組三階段神經回饋實驗評估此一行動神經回饋訓練系統的效能。

Recording of physiological signals plays an important role in the biomedical research and clinical fields. In this study, two embedded systems related to physiological signal recording were proposed. Both systems are based on microcontrollers (MCUs) and wireless technologies.
The first system is a wireless modularized Polysomnography (PSG) system. PSG continuously and simultaneously records multiple physiological signals during the sleep of the subject, and it is commonly used in hospitals or sleep centers to diagnose sleep disorders. The excessive number of wired connections for traditional PSG is often a problem that leads to sleep disturbance. The proposed wireless PSG system is composed of multiple tiny, low-cost and wireless-synchronized signal acquisition nodes, and each node acquires specific physiological signals within a small body region to reduce sleep disturbance resulting from recording wires. To evaluate accuracy, the system and a commercial PSG system were mounted on subjects to simultaneously perform overnight recording, and the recorded data were compared. The results show that, in addition to high consistency (>93%) with the reference system, due to the reduction of the disturbance from recording wires, the proposed system has better comfortableness performance in terms of several objective and subjective sleep indices.
The second system is a wireless mobile neurofeecback training system. Neurofeedback training is recently used in enhancement of cognitive function or amelioration of clinical symptoms. Most available neurofeedback systems are a laboratory design, which contains wires to the training machine, resulting in inconvenience for subjects. The proposed wireless neurofeecback system contains an EEG signal analysis device and a smartphone connected based on the Bluetooth low energy (BLE) technology. A three-stage neurofeedback experiment was performed to evaluate the performance of the mobile neurofeecback training system.

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