@inproceedings{chen_wavelet-based_2015,
	address = {Milano, IT},
	title = {Wavelet-{Based} {Motion} {Artifact} {Removal} for {Electrodermal} {Activity}},
	url = {http://affect.media.mit.edu/pdfs/15.Chen-etal-EMBC.pdf},
	abstract = {Electrodermal activity (EDA) recording is a powerful,
widely used tool for monitoring psychological or physiological
arousal. However, analysis of EDA is hampered by
its sensitivity to motion artifacts. We propose a method for
removing motion artifacts from EDA, measured as skin conductance
(SC), using a stationary wavelet transform (SWT).
We modeled the wavelet coefficients as a Gaussian mixture
distribution corresponding to the underlying skin conductance
level (SCL) and skin conductance responses (SCRs). The
goodness-of-fit of the model was validated on ambulatory SC
data. We evaluated the proposed method in comparison with
three previous approaches. Our method achieved a greater
reduction of artifacts while retaining motion-artifact-free data.},
	language = {English},
	booktitle = {Proceedings {International} {Conference} of the {IEEE} {Engineering} in {Medicine} and {Biology} {Society}},
	author = {Chen, Weixuan and Jaques, Natasha and Taylor, Sara and Sano, Akane and Fedor, Szymon and Picard, Rosalind W.},
	month = aug,
	year = {2015},
	keywords = {E4, Q sensor}
}