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I am looking for the state-of-the-art in stationarity testing. I have a number of streams of data acquired from EMG recordings and I would like to know which one is the most "stable" or stationary among the streams. I've read a number of articles and presentations, for example here and here. Since such signals are contaminated by a high amount of noise and may include various artifacts, such as body motion artifacts, I am not sure which methods are valid and robust. I would like to know if anyone has hands-on experience with such statistical measures and can point me in the best direction. Thank you!

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The probability density function (pdf) of an electromyography (EMG) signal provides useful information for choosing an appropriate feature extraction technique. The pdf is influenced by many factors, including the level of contraction force, muscle type, and noise. In this paper, we investigated the pdfs of noisy EMG signals artificially contaminated with five different noise types: 1) Electrocardiography (ECG) interference; 2) many spurious background spikes; 3) white Gaussian noise; 4) motion artifact; and 5) power line interference at various levels of signal-to-noise ratio (SNR)

detail explanation on probability density function for Stationary

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