EEMD and bidimensional RLS to suppress physiological interference for heterogeneous distribution in fNIRS study

Yan Zhang; Dan Liu; Qisong Wang; Xin Liu; Chunling Yang; Jinwei Sun; Jingyang Lu; Peter Rolfe

doi:10.1142/s1793545818500396

[1] G. Morren, M. Wolf, P. Lemmerling, U. Wolf, J. H. Choi, E. Gratton, L. D. Lathauwer, S. V. Huffel, “Detection of fast neuronal signals in the motor cortex from functional near infrared spectroscopy measurements using independent component analysis,” Med. Biol. Eng. Comput. 42 (1), 92–99 (2004). Crossref, Google Scholar

[2] D. Liu, X. Liu, Y. Zhang, Q. Wang, J. Lu, J. Sun, “Imitation-tumor targeting based on continuous-wave near-infrared tomography,” Comput. Assisted Surg. 22 (Supp 1), 157–162 (2017). Crossref, Google Scholar

[3] D. Liu, X. Liu, Y. Zhang, Q. Wang, J. Lu, “Tissue phantom-based breast cancer detection using continuous near-infrared sensor,” Bioengineered 7 (5), 321–326 (2016). Crossref, Google Scholar

[4] E. Sakakibara, F. Homae, S. Kawasaki et al., “Detection of resting state functional connectivity using partial correlation analysis: A study using multidistance and whole-head probe near-infrared spectroscopy,” NeuroImage 142, 90–601 (2016). Crossref, Google Scholar

[5] Y. Zhang, J. Sun, P. Rolfe, “RLS adaptive filtering for physiological interference reduction in NIRS brain activity measurement: A Monte Carlo study,” Physiol. Meas. 33, 925–942 (2012). Crossref, Google Scholar

[6] X. Zhang, J. A. Noah, S. Dravida, J. Hirsch, “Signal processing of functional NIRS data acquired during overt speaking,” Neurophotonics 4 (4), 041409 (2017). Crossref, Google Scholar

[7] L. Gagnon, M. A. Yücel, D. A. Boas, R. J. Cooper, “Further improvement in reducing superficial contamination in NIRS using double short separation measurements,” Neuroimage 85 (1), 127–135 (2014). Crossref, Google Scholar

[8] M. A. Yücel, J. Selb, C. M. Aasted, P. Lin, D. Borsook, L. Becerra, D. A. Boas, “Mayer waves reduce the accuracy of estimated hemodynamic response functions in functional near-infrared spectroscopy,” Biomed. Opt. Express 7 (8), 3078–3088 (2016). Crossref, Google Scholar

[9] S. Umeyama, T. Yamada, “Monte Carlo study of global interference cancellation by multidistance measurement of near-infrared spectroscopy,” J. Biomed. Opt. 14 (6), 064025 (2009). Crossref, Google Scholar

[10] S. G. Diamond, T. J. Huppert, V. Kolehmainen, M. A. Franceschini, J. P. Kaipio, S. R. Arridge, D. A. Boas, “Physiological system identification with the Kalman filter in diffuse optical tomography,” Med. Image Comput. Comput. Assisted Interv. 8, 649–656 (2005). Google Scholar

[11] S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48, 1491–1504 (2003). Crossref, Google Scholar

[12] H. Obrig, M. Neufang, R. Wenzel, M. Kohl, J. Steinbrink, K. Einhaupl, A. Villringer, “Spontaneous low frequency oscillations of cerebral hemodynamics and metabolism in human adults,” Neuroimage 12 (6), 623–639 (2000).

[13] Y. H. Zhang, D. H. Brooks, M. A. Franceschini, D. A. Boas. “Eigenvector-based spatial filtering for reduction of physiological interference in diffuse optical imaging,” J. Biomed. Opt. 10 (1), 011014 (2005).

[14] S. Kohno, I. Miyai, A. Seiyama, I. Oda, A. Ishikawa, S. Tsuneishi, T. Amita, K. Shimizu, “Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis,” J. Biomed. Opt. 12 (6), 062111 (2007). Crossref, Google Scholar

[15] Q. Zhang, E. N. Brown, G. E. Strangman, “Adaptive filtering for global interference cancellation and real-time recovery of evoked brain activity: A Monte Carlo simulation study,” J. Biomed. Opt. 12, 044014 (2007).

[16] Q. Zhang, E. N. Brown, G. E. Strangman, “Adaptive filtering to reduce global interference in evoked brain activity detection: A human subject case study,” J. Biomed. Opt. 12, 064009 (2007).

[17] R. Saager, A. Berger, “Measurement of layer-like hemodynamic trends in scalp and cortex: Implications for physiological baseline suppression in fuctional near-infrared spectroscopy,” J. Biomed. Opt. 13 (3), 03417 (2008). Crossref, Google Scholar

[18] H. Liang, Z. Lin, R. W. McCallum, “Artifact reduction in electrogastrogram based on empirical mode decomposition method,” Med. Biol. Eng. Comput. 38, 35–41 (2000). Crossref, Google Scholar

[19] X. Yin, B. Xu, C. Jiang, Y. Fu, Z. Wang, H. Li, G. Shi, “NIRS-based classification of clench force and speed motor imagery with the use of empirical mode decomposition for BCI,” Med. Eng. Phys. 37, 280–286 (2015). Crossref, Google Scholar

[20] Z. Wu, N. E. Huang, “Ensemble empirical mode decomposition: A noise-assisted data analysis method,” Adv. Adapt. Data Anal. 1, 1–41 (2009). Link, Google Scholar

[21] Y. Zhang, J. Sun, P. Rolfe, “Reduction of global interference in functional multidistance near-infrared spectroscopy using empirical mode decomposition and recursive least squares: A Monte Carlo study,” J. Eur. Opt. Soc. Rapid Publ. 6, 11033 (2011).