Artifacts, removal

Artifact removal and/or linearization. A common form of artifact removal is baseline correction of a spectrum or chromatogram. Common linearizations are the conversion of spectral transmittance into spectral absorbance and the multiplicative scatter correction for diffuse reflectance spectra. We must be very careful when attempting to remove artifacts. If we do not remove them correctly, we can actually introduce other artifacts that are worse than the ones we are trying to remove. But, for every artifact that we can correctly remove from the data, we make available additional degrees-of-freedom that the model can use to fit the relationship between the concentrations and the absorbances. This translates into greater precision and robustness of the calibration. Thus, if we can do it properly, it is always better to remove an artifact than to rely on the calibration to fit it. Similar reasoning applies to data linearization. 

A. Devaraj, M. Izzetoglu, K. Izzetoglu, S. C. Bunce, C. Y. Li, and B. Onaral. Motion artifact removal in FNIR spectroscopy for real-world applications. In Nondestructive Sensing for Food Safety, Quality, and Natural Resources. Edited by Chen, Yud-Ren Tu, Shu-I. Proceedings of the SPIE, Volume 5588, pp. 22f-229 (2004)., pages 224-229, October 2004. 

For FTS data, artifact removal is a consideration that is as important as resolution improvement for most researchers in this field. Interferogram continuation methods are not as yet widely known in this area. Methods currently in widespread use that are effective in artifact removal involve the multiplication of the interferogram by various window functions, an operation called apodization. A carefully chosen window function can be very effective in suppressing the artifacts. However, the peaks are almost always broadened in the process. This can be understood from the uncertainty principle. A window that reduces the function most strongly closest to the end points will yield a transform for the modified function that must be broader than it was originally. Alternatively we may employ the convolution... 

KeUer, T. and Popovid, M.R., Real-time stimulation artifact removal in EMG signals for neural prosthesis control appHcations. Proceedings of 6th Annual IFESS Conference, Cleveland, OH, June 10-13,... 

Sweeney, K. T., Ayaz, H., Ward, T. E., Izzetoglu, M., McLoone, S. R, and Onaral, B. (2012a). A methodology for validating artifact removal techniques for physiological signals. IEEE Transactions on Information Technology in Biomedicine, 16(5), 918-26. doi 10.1109/TITB.2012.2207400. 

Tang X, Ning R, Yu R, Conover D. Cone beam volume CT image artifacts caused by defective cells in X-ray flat panel imagers and the artifact removal using a wavelet-analysis-based algorithm. Med Phys 2001 28(5) 812-825. 

S. Mehrkanoon, et al., "Real time ocular and facial muscle artifacts removal from EEG signals using LMS adaptive algorithm," Kuala Lumpur, 2007, pp. 1245-1250. 

The EOG and EMG artifacts were removed before the EEG data was further processed. An automatic EEG artifact removal method that combines the Blind Source Separation method with the Wavelet method is used here. The EEG signals are first decomposed into independent components using the Second Order Blind Identification (SOBI)... 

The Fourier Transform is use to obtain the frequency component of the EEG data. The EEG that has been cleaned using the artifact removal method and enhanced as stated above is then segmented into one second segments. The Fourier Transform for each segment is then found. 

Based on Fig. 2, we can see the effects of artifact removal method on to the EEG. It can be seen that from 0-0.5 seconds, the EEG did not change since there are no EOG artifacts there. The changes only occur at the region where the EOG artifacts existed. 

The automatic artifact removal method seems to perform quite well in removing artifacts while maintaining the EEG signal. This is critical as most artifact method remove EEG signal along with the artifacts. 

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