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Technological Advances

Frequently, research is limited by the technologies available. Efforts towards overcoming current limits, by design of new hardware and software solutions, is therefore much appreciated. Research aiming for technological advance constantly pushes forward and creates a wide range of new possibilities to be explored by the whole scientific community.

R. A. Khan, N. Naseer, and M. J. Khan, “Chapter 13 - Drowsiness Detection During a Driving Task Using fNIRS,” in Neuroergonomics, H. Ayaz and F. Dehais, Eds. Academic Press, 2019, pp. 79–85.

B. Wortelen, A. Unni, J. W. Rieger, A. Lüdtke, and J.-P. Osterloh, “Monte Carlo Methods for Real-Time Driver Workload Estimation Using a Cognitive Architecture,” in Cognitive Infocommunications, Theory and Applications, R. Klempous, J. Nikodem, and P. Z. Baranyi, Eds. Cham: Springer International Publishing, 2019, pp. 25–48.

B. Blanco, M. Molnar, and C. Caballero-Gaudes, “Effect of prewhitening in resting-state functional near-infrared spectroscopy data,” NPh, vol. 5, no. 4, p. 040401, Oct. 2018.

A. Janani and M. Sasikala, “Evaluation of classification performance of functional near infrared spectroscopy signals during movement execution for developing a brain-computer interface application using optimal channels,” J. Near Infrared Spectrosc., JNIRS, vol. 26, no. 4, pp. 209–221, Aug. 2018. 

L. Duan, Z. Zhao, Y. Lin, X. Wu, Y. Luo, and P. Xu, “Wavelet-based method for removing global physiological noise in functional near-infrared spectroscopy,” Biomed. Opt. Express, BOE, vol. 9, no. 8, pp. 3805–3820, Aug. 2018.

S. E. Kober, V. Hinterleitner, G. Bauernfeind, C. Neuper, and G. Wood, “Trainability of hemodynamic parameters: A near-infrared spectroscopy based neurofeedback study,” Biological Psychology, vol. 136, pp. 168–180, Jul. 2018.

L. M. Hocke, I. K. Oni, C. C. Duszynski, A. V. Corrigan, B. deB Frederick, and J. F. Dunn, “Automated Processing of fNIRS Data—A Visual Guide to the Pitfalls and Consequences,” Algorithms, vol. 11, no. 5, p. 67, May 2018.

R. Gupta, A. Avila, and T. H. Falk, “Towards a Neuro-Inspired No-Reference Instrumental Quality Measure for Text-to-Speech Systems,” in 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX), 2018, pp. 1–6.

L. Zhu, S. Haghani, and L. Najafizadeh, “Spatiotemporal Characterization of Brain Function Via Multiplex Visibility Graph,” in Biophotonics Congress: Biomedical Optics Congress 2018 (Microscopy/Translational/Brain/OTS) (2018), paper JTh3A.54, 2018, p. JTh3A.54.

O. Klempíř, R. Krupička, and R. Jech, “MEDIAN METHOD FOR DETERMINING CORTICAL BRAIN ACTIVITY IN A NEAR INFRARED SPECTROSCOPY IMAGE,” Lékař a technika - Clinician and Technology, vol. 48, no. 1, pp. 11–16, Mar. 2018.

L. R. Trambaiolli, C. E. Biazoli, A. M. Cravo, and J. R. Sato, “Predicting affective valence using cortical hemodynamic signals,” Scientific Reports, vol. 8, no. 1, p. 5406, Mar. 2018.

M. D. Pfeifer, F. Scholkmann, and R. Labruyère, “Signal Processing in Functional Near-Infrared Spectroscopy (fNIRS): Methodological Differences Lead to Different Statistical Results,” Front. Hum. Neurosci., vol. 11, 2018.

M. A. Kamran, M. M. Naeem Mannan, and M.-Y. Jeong, “Initial-Dip Existence and Estimation in Relation to DPF and Data Drift,” Front. Neuroinform., vol. 12, 2018.

A. Janani and M. Sasikala, “Classification of fNIRS Signals for Decoding Right- and Left-Arm Movement Execution Using SVM for BCI Applications,” in Computational Signal Processing and Analysis, 2018, pp. 315–323.

J. Gemignani, E. Middell, R. L. Barbour, H. L. Graber, and B. Blankertz, “Improving the analysis of near-infrared spectroscopy data with multivariate classification of hemodynamic patterns: a theoretical formulation and validation,” J. Neural Eng., vol. 15, no. 4, p. 045001, 2018.

G. A. Zimeo Morais, J. B. Balardin, and J. R. Sato, “fNIRS Optodes’ Location Decider (fOLD): a toolbox for probe arrangement guided by brain regions-of-interest,” Scientific Reports, vol. 8, no. 1, Dec. 2018.

G. A. Zimeo Morais et al., “Non-neuronal evoked and spontaneous hemodynamic changes in the anterior temporal region of the human head may lead to misinterpretations of functional near-infrared spectroscopy signals,” Neurophotonics, vol. 5, no. 01, p. 1, Aug. 2017.

J. B. Balardin, G. A. Z. Morais, R. A. Furucho, L. R. Trambaiolli, and J. R. Sato, “Impact of communicative head movements on the quality of functional near-infrared spectroscopy signals: negligible effects for affirmative and negative gestures and consistent artifacts related to raising eyebrows,” J. Biomed. Opt, vol. 22, no. 4, pp. 046010–046010, 2017.

N. K. Qureshi, N. Naseer, F. M. Noori, H. Nazeer, R. A. Khan, and S. Saleem, “Enhancing Classification Performance of Functional Near-Infrared Spectroscopy- Brain–Computer Interface Using Adaptive Estimation of General Linear Model Coefficients,” Frontiers in Neurorobotics, vol. 11, Jul. 2017.

L. Pollonini, H. Bortfeld, and J. S. Oghalai, “PHOEBE: a method for real time mapping of optodes-scalp coupling in functional near-infrared spectroscopy,” Biomed. Opt. Express, BOE, vol. 7, no. 12, pp. 5104–5119, Dec. 2016.

H.-D. Nguyen and K.-S. Hong, “Bundled-optode implementation for 3D imaging in functional near-infrared spectroscopy,” Biomed Opt Express, vol. 7, no. 9, pp. 3491–3507, Aug. 2016.

L. Holper, E. Seifritz, and F. Scholkmann, “Short-term pulse rate variability is better characterized by functional near-infrared spectroscopy than by photoplethysmography,” J Biomed Opt, vol. 21, no. 9, p. 91308, Sep. 2016.

J. Yao, F. Tian, Y. Rakvongthai, S. Oraintara, and H. Liu, “Quantification and normalization of noise variance with sparsity regularization to enhance diffuse optical tomography,” Biomed Opt Express, vol. 6, no. 8, pp. 2961–2979, Aug. 2015.

D. Piao, R. L. Barbour, H. L. Graber, and D. C. Lee, “On the geometry dependence of differential pathlength factor for near-infrared spectroscopy. I. Steady-state with homogeneous medium,” J. Biomed. Opt, vol. 20, no. 10, pp. 105005–105005, 2015.

H. D. Nguyen and K. S. Hong, “Multiple optodes configuration for measuring the absolute hemodynamic response using spatially resolved spectroscopy method: An fNIRS study,” in 2015 15th International Conference on Control, Automation and Systems (ICCAS), 2015, pp. 1827–1832.

M. A. Kamran, M. Y. Jeong, and M. M. N. Mannan, “Optimal hemodynamic response model for functional near-infrared spectroscopy,” Front Behav Neurosci, vol. 9, Jun. 2015.

E. E. Vidal-Rosas et al., “Reduced-order modeling of light transport in tissue for real-time monitoring of brain hemodynamics using diffuse optical tomography,” J Biomed Opt, vol. 19, no. 2, p. 26008, Feb. 2014.

I. W. Selesnick, H. L. Graber, D. S. Pfeil, and R. L. Barbour, “Simultaneous Low-Pass Filtering and Total Variation Denoising,” IEEE Transactions on Signal Processing, vol. 62, no. 5, pp. 1109–1124, Mar. 2014.

M. A. Kamran and K.-S. Hong, “Reduction of physiological effects in fNIRS waveforms for efficient brain-state decoding,” Neurosci. Lett., vol. 580, pp. 130–136, Sep. 2014.

N. Hemmati Berivanlou, S. K. Setarehdan, and H. Ahmadi Noubari, “Evoked hemodynamic response estimation using ensemble empirical mode decomposition based adaptive algorithm applied to dual channel functional near infrared spectroscopy (fNIRS),” Journal of Neuroscience Methods, vol. 224, pp. 13–25, Mar. 2014.

C. Habermehl, J. Steinbrink, K.-R. Müller, and S. Haufe, “Optimizing the regularization for image reconstruction of cerebral diffuse optical tomography,” J Biomed Opt, vol. 19, no. 9, p. 96006, Sep. 2014.

V. C. Kavuri, Z.-J. Lin, F. Tian, and H. Liu, “Sparsity enhanced spatial resolution and depth localization in diffuse optical tomography,” Biomed Opt Express, vol. 3, no. 5, pp. 943–957, Apr. 2012.

C. Habermehl et al., “Somatosensory activation of two fingers can be discriminated with ultrahigh-density diffuse optical tomography,” Neuroimage, vol. 59, no. 4, pp. 3201–3211, Feb. 2012.

M. Aqil, K.-S. Hong, M.-Y. Jeong, and S. S. Ge, “Detection of event-related hemodynamic response to neuroactivation by dynamic modeling of brain activity,” Neuroimage, vol. 63, no. 1, pp. 553–568, Oct. 2012.

X.-S. Hu, K.-S. Hong, S. S. Ge, and M.-Y. Jeong, “Kalman estimator- and general linear model-based on-line brain activation mapping by near-infrared spectroscopy,” Biomed Eng Online, vol. 9, p. 82, 2010.

A. Bluestone, G. Abdoulaev, C. Schmitz, R. Barbour, and A. Hielscher, “Three-dimensional optical tomography of hemodynamics in the human head,” Opt Express, vol. 9, no. 6, pp. 272–286, Sep. 2001.

R. L. Barbour, H. L. Graber, J. Chang, S.-L. S. Barbour, P. C. Koo, and R. Aronson, “MRI-Guided Optical Tomography: Prospects and Computation for a New Imaging Method,” IEEE Comput. Sci. Eng., vol. 2, no. 4, pp. 63–77, Dec. 1995.

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