背景與目的：在一般的實驗中使用常使用可調放大倍率的放大器與被動性電極來擷取肌電圖(electromyographics, EMG)；另一方面，具有前置放大與不鏽鋼金屬電極板的主動電極也常用於肌動學研究與輔具控制上。因此這裡衍生出一個重要的問題：「我們是否能以在被動電極所發展的肌電訊號分析演算法套用在主動電極記錄的肌電訊號上？」在這個研究中，我們嘗試在頻譜分析上解答這個問題。方法：5個正常受試者參與這個研究。自我迴歸參數(autoregressive coefficient, AR coefficient)用來做為自受試者右上臂所量測的主動電極與被動電極之EMG頻譜特徵參數。最大近似距離量測(maximal likelihood distance measurement)用來分辨不同的頻譜參數間的差別。結果：結果顯示在被動電極的頻譜參數的預估可判別率高於主動電極（被動電極88.5%，主動電極85.84%），然而在統計上並沒有顯著不同(pair-t test, p>0.05)。結論：本研究結果顯示不論何種電極記錄的肌電圖之頻譜差異應可忽略。

Background and Purpose: Using the passive electrodes and amplifiers with adjustable gain is very popular in laboratory for Electromyographics (EMG) measurement. However, the active electrodes, which contain pre-amplifiers and stainless-steel plates, are used in most clinical applications of kinesiological study and assistive devices control. There remains an important question: "Can we use the algorithm developed by the passive electrodes in the applications by the active electrodes?" In this study, we try to answer this question by power spectrum analysis. Methods: Five able-body subjects were recruited in this study. The autoregressive (AR) coefficient was used to represent the spectral features of EMG signals of right upper arm via both active and passive electrodes. Maximal likelihood distance measure measurement was used to classify the difference of these spectral features. Results: It showed the estimated recognition rates of these spectral features in the passive electrodes were higher than those in the active ones (averaged recognition rate 88.5% in passive electrode recording and 85.84% in active electrode recording). However, there are not significant differences between them in statistics. Conclusion: These results imply that the differences of power spectrum that recorded via both electrodes could be negligible.