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    題名: 腦電圖(EEG)信號擷取系統設計
    其他題名: Electroencephalography (EEG) Signal Acquisition System Design
    作者: 撒拉夫丁
    MORSALIN, S M SALAHUDDIN
    貢獻者: 科技學院永續綠色科技碩士學位學程
    賴信志
    LAI, SHIN-CHI
    關鍵詞: 印刷電路板;類比數位轉換器;腦計算機接口;腦電圖;放大器和電極
    Amplifier and Electrodes;Printed Circuit Board (PCB);Analog-to-Digital Converter (ADC);Brain Computer Interface (BCI);OPAMP;Electroencephalography (EEG)
    日期: 2019
    上傳時間: 2022-05-23 15:35:26 (UTC+8)
    摘要:   為了解腦電活動,腦電 (EEG) 信號記錄了頭皮間所發出的微弱信號,也提供大腦動態活動表現的波形。本研究致力於電路設計和設備開發,用以實現所需之腦波電 (EEG) 信號擷取系統。採集信號需要一定強度的幅度,通常以毫伏為單位表示 (millivolts)。一般而言,數據擷取過程含了三個階段:1)原始EEG信號的擷取可以通過有源電極和具有較小增益的儀表放大器完成;2)使用帶通濾波器和帶斥濾波器改善信號質量;3)將EEG信號透過內嵌於微控制器的類比-數位轉換器(ADC)來轉換成數位碼。數位碼可再儲存於記憶體中,並由藍牙模組進一步傳輸。實驗結果說明該系統可以有效地實現EEG信號的擷取和存儲。設計的印刷電路板(PCB)尺寸小於5×5 cm2。所提出之系統將有益於所有參與使用EEG進行臨床診斷和監測人員,腦機介面開發。
      Electroencephalography (EEG) signals are recorded for knowing the electrical activity of brain from the scalp, and the recorded waveform provides acquits into the dynamic aspects of brain activity. This study incorporates the circuit design and device development to achieve the Electroencephalography (EEG) signal acquisition front-end circuit design for future Brain Computer Interface (BCI) applications. The amplitude of acquired signals should be strong enough and is usually expressed in unit of millivolts. The data acquisition procedure consists three stages: 1) The acquisition of original EEG signal can be done by the active electrode and an instrumentation amplifier with a smaller gain; 2) Improves the signal quality by using band-pass filter and band-stop filter; 3) Those EEG signals were converted into the digital code through the analog-to-digital converter (ADC) that was integrated to a micro-controller. The digital code is stored into an embedded memory, and is further transmitted via Bluetooth module. The experimental results show that the system could implement the acquisition and storage of the EEG signals efficiently. The size of printed circuit board (PCB) for the proposed deign is smaller than 5×5 cm2. This system would be benefit to all involve in the use of EEG for clinical diagnosis and monitoring, or even for brain computer interface.
    顯示於類別:[永續綠色科技碩士學位學程] 博碩士論文

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