@article{76d5b7e2ff43473480545e424558486e,
title = "Identification of Contaminant Type in Surface Electromyography (EMG) Signals",
keywords = "AWGN, electromyography, medical signal processing, signal classification, support vector machines, EMG-enabled rehabilitation system, additive white Gaussian noise, amplifier saturation, classification system, contaminant type identification, electrocardiogram interference, motion artifact, power line interference, real EMG signals, signal quality, signal-to-noise ratio, simulated EMG signals, surface EMG signals, surface electromyography signals, Contamination, Electrocardiography, Electromyography, Interference, Muscles, Signal to noise ratio, Support vector machines, Biosignal quality analysis, classification, electromyography (EMG), myoelectric signals, prostheses",
author = "P. McCool and G.D. Fraser and A.D.C. Chan and L. Petropoulakis and J.J. Soraghan",
note = "INSPEC Accession Number: 14446359",
year = "2014",
month = jan,
day = "21",
doi = "10.1109/TNSRE.2014.2299573",
language = "English",
volume = "22",
pages = "774--783",
journal = "IEEE Transactions on Neural Systems and Rehabilitation Engineering",
issn = "1534-4320",
publisher = "IEEE",
number = "4",
}