[1]
|
Ertelt, D., et al. (2007) Action Observation Has a Positive Impact on Rehabilitation of Motor Deficits after Stroke. NeuroImage, 36, 164-173. http://dx.doi.org/10.1016/j.neuroimage.2007.03.043
|
[2]
|
De. Vries, S. and Mulder, T. (2007) Motor Imagery and Stroke Rehabilitation: A Critical Discussion. Journal of Rehabilitation Medicine, 37, 5-13. http://dx.doi.org/10.2340/16501977-0020
|
[3]
|
Coyle, S., et al. (2004) On the Suitability of Near-Infrared (NIR) Systems for Next-Generation Brain-Computer Interfaces. Physiological Measurement, 25, 815-822. http://dx.doi.org/10.1088/0967-3334/25/4/003
|
[4]
|
Jöbsis, F.F. (1977) Noninvasive, Infrared Monitoring of Cerebral and Myocardial Oxygen Sufficiency and Circulatory Parameters. Science, 198, 1264-1267. http://dx.doi.org/10.1126/science.929199
|
[5]
|
Ferrari, M., Binzoni, T. and Quaresima, V. (1997) Oxidative Metabolism in Muscle. Philosophical Transactions of the Royal Society B, 352, 677-683. http://dx.doi.org/10.1098/rstb.1997.0049
|
[6]
|
Gratton, G. and Corballis, P.M. (1995) Removing the Heart from the Brain: Compensation for the Pulse Artifact in the Photon Migration Signal. Psychophysiology, 32, 292-299. http://dx.doi.org/10.1111/j.1469-8986.1995.tb02958.x
|
[7]
|
Morren, G., et al. (2004) Detection of Fast Neuronal Signals in the Motor Cortex from Functional near Infrared Spectroscopy Measurements Using Independent Component Analysis. Medical & Biological Engineering & Computing, 42, 92-99. http://dx.doi.org/10.1007/BF02351016
|
[8]
|
Victor, S., Jonatan, L. and Miroslav, V. (2013) Adaptive Filter Support Selection for Signal Denoising Based on the Improved ICI Rule. Digital Signal Processing, 23, 65-74. http://dx.doi.org/10.1016/j.dsp.2012.06.014
|
[9]
|
Izzetoglu, M., et al. (2005) Motion Artifact Cancellation in NIR Spectroscopy Using Wiener Filtering. IEEE Transactions on Biomedical Engineering, 52, 934-938. http://dx.doi.org/10.1109/TBME.2005.845243
|
[10]
|
Quan, Z., et al. (2009) Adaptive Filtering to Reduce Global Interference in Non-Invasive NIRS Measures of Brain Activation: How Well and When Does It Work? Neuroimage, 45, 788-794. http://dx.doi.org/10.1016/j.neuroimage.2008.12.048
|
[11]
|
Robertson, F.C., Douglas, T.S. and Meintjes, E.M. (2010) Motion Artifact Removal for Functional Near-Infrared Spectroscopy: A Comparison of Methods. IEEE Transactions on Biomedical Engineering, 57, 1377-1387. http://dx.doi.org/10.1109/TBME.2009.2038667
|
[12]
|
Hiroki, S., et al. (2006) Within-Subject Reproducibility of Near-Infrared Spectroscopy Signals in Sensorimotor Activation after 6 Months. Journal of Biomedical Optics, 11, 14-21.
|
[13]
|
Molaviet, B. and Dumont, G.A. (2012) Wavelet-Based Motion Artifact Removal for Functional Near-Infrared Spectroscopy. Physiological Measurement, 33, 259-270. http://dx.doi.org/10.1088/0967-3334/33/2/259
|
[14]
|
Jang, K.E., et al. (2009) Wavelet Minimum Description Length Detrending for Near-Infrared Spectroscopy. Journal of Biomedical Optics, 14, 1-13. http://dx.doi.org/10.1117/1.3127204
|
[15]
|
Strangman, G., Franceschini, M.A. and Boas, D.A. (2003) Factors Affecting the Accuracy of Near-Infrared Spectroscopy Concentration Calculations for Focal Changes in Oxygenation Parameters. Neuroimage, 18, 865-879. http://dx.doi.org/10.1016/S1053-8119(03)00021-1
|
[16]
|
Maryam, A. and Hamidreza, A. (2013) Statistical Modeling and Denoising Wigner-Ville Distribution. Digital Signal Processing, 23, 506-513. http://dx.doi.org/10.1016/j.dsp.2012.08.016
|
[17]
|
Izzetoglu, M., et al. (2010) Motion Artifact Cancellation in NIR Spectroscopy Using Discrete Kalman Filtering. Biomedical Engineering Online, 9, 16. http://dx.doi.org/10.1186/1475-925X-9-16
|
[18]
|
Behnam, M. and Guy, A.D. (2012) Wavelet-Based Motion Arti-fact Removal for Functional Near-Infrared Spectroscopy. Physiological Measurement, 33, 259-270. http://dx.doi.org/10.1088/0967-3334/33/2/259
|
[19]
|
Engreitz, J., et al. (2010) Independent Component Analysis: Mining Microarray Data for Fundamental Human Gene Expression Modules. Journal of Biomedical Informatics, 43, 932-944. http://dx.doi.org/10.1016/j.jbi.2010.07.001
|
[20]
|
Hyvarinen, A. and Oja, E. (2000) Independent Component Analysis: Algorithm and Applications. Neural Networks, 13, 411-430. http://dx.doi.org/10.1016/S0893-6080(00)00026-5
|
[21]
|
Langlois, D., Chartier, S. and Gosselin, D. (2010) An Introduction to Independent Component Analysis: InfoMax and FastICA Algorithm. Tutorials in Quantitative Methods for Psychology, 6, 31-38.
|
[22]
|
Toivianen, M., Corona, F., Paaso, J. and Teppola, P. (2010) Blind Source Separation in Diffuse Reflectance NIR Spectroscopy Using Independent Component Analysis. Journal of Chemometrics, 24, 514-522. http://dx.doi.org/10.1002/cem.1316
|
[23]
|
Himberg, J., Hyvärinen, A. and Esposito, F. (2010) Validating the Independent Components of Neuroimaging Time Series via Clustering and Visualization. NeuroImage, 22, 1214-1222. http://dx.doi.org/10.1016/j.neuroimage.2004.03.027
|
[24]
|
Tomé, A.M., Teixeira, A.R., Lang, E.W., Stadlthanner, K., Rocha, A.P. and Almeida, R. (2005) dAMUSE—A New Tool for Denoising and Blind Source Separation. Digital Signal Processing, 15, 400-421. http://dx.doi.org/10.1016/j.dsp.2005.01.004
|
[25]
|
Aminghafari, M., Cheze, N. and Poggi, J.M. (2006) Multivariate Denoising Using Wavelets and Principal Component Analysis. Computational Statistics & Data Analysis, 50, 2381-2398. http://dx.doi.org/10.1016/j.csda.2004.12.010
|
[26]
|
Bakshi, B. (1996) Multiscale PCA with Application to MSPC Monitoring. AIChE Journal, 44, 1596-1610. http://dx.doi.org/10.1002/aic.690440712
|
[27]
|
Elise, M., Truntzer, C., Cardot, H. and Ducoroy, P. (2010) Multivariate Denoising Methods Combining Wavelets and Principal Component Analysis for Mass Spectrometry Data. PROTEOMICS, 10, 2564-2572. http://dx.doi.org/10.1002/pmic.200900185
|
[28]
|
Yang, R.G. and Ren, M.W. (2011) Wavelet Denoising Using Principal Component Analysis. Expert Systems with Applications-ESWA, 38, 1073-1076.
|
[29]
|
Zima, M., Tichavsky, P., Paul, K. and Krajca, V. (2012) Robust Removal of Short-Duration Artifacts in Long Neonatal EEG Recordings Using Wavelet-Enhanced ICA and Adaptive Combining of Tentative Reconstructions. Physiological Measurement, 33, N39-N49. http://dx.doi.org/10.1088/0967-3334/33/8/N39
|
[30]
|
Rebeca, R., Vélez-Pérez, H., Ranta, R., Louis Dorr, V., Maquin, D. and Maillard, L. (2012) Blind Source Separation, Wavelet Denoising and Discriminant Analysis for EEG Artefacts and Noise Cancelling. Biomedical Signal Processing and Control, 7, 389-400. http://dx.doi.org/10.1016/j.bspc.2011.06.005
|
[31]
|
Mathworks (2012) Matlab Software. R2012a Version.
|
[32]
|
Chaddad, A., et al. (2012) Optical Receiver Front-End Intended for a Detector of Near Infrared Spectroscopy System. Journal of Sensor Networks, 2, 24-31.
|
[33]
|
Chaddad, A., Kamrani, E., Le Lan, J. and Sawan, M. (2013) Denoising fNIRS Signals to Enhance Brain Imaging Diagnosis. 29th Southern Biomedical Engineering Conference, Miami, 3-5 May 2013, 33-34.
|