Abstract

In this paper, the use of initial dips using functional near-infrared spectroscopy (fNIRS) for brain-computer interface (BCI) is investigated. Features and window sizes for detecting initial dips are also discussed. Three mental tasks including mental arithmetic, mental counting, and puzzle solving are performed in obtaining fNIRS signals from the prefrontal cortex. Vector-based phase analysis method combined with a threshold circle, as a decision criterion, are used to detect the initial dips. Eight healthy subjects participate in experiment. Linear discriminant analysis is used as a classifier. To classify initial dips, five features (signal mean, peak value, signal slope, skewness, and kurtosis) of oxy-hemoglobin (HbO) and four different window sizes (0~1, 0~1.5, 0~2, and 0~2.5 sec) are examined. It is shown that a combination of signal mean and peak value and a time period of 0~2.5 sec provide the best average classification accuracy of 57.5% for three classes. To further validate the result, three-class classification using the conventional hemodynamic response (HR) is also performed, in which two features (signal mean and signal slope) and 2~7 sec window size have yielded the average classification accuracy of 65.9%. This reveals that fNIRS-based BCI using initial dip detection can reduce the command generation time from 7 sec to 2.5 sec while the classification accuracy is a bit sacrificed from 65.9% to 57.5% for three mental tasks. Further improvement can be made by using deoxy hemoglobin signals in coping with the slow HR problem.

© 2016 Optical Society of America

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  1. R. D. Frostig, E. E. Lieke, D. Y. Ts’o, and A. Grinvald, “Cortical functional architecture and local coupling between neuronal activity and the microcirculation revealed by in vivo high-resolution optical imaging of intrinsic signals,” Proc. Natl. Acad. Sci. U.S.A. 87(16), 6082–6086 (1990).
    [Crossref] [PubMed]
  2. A. Grinvald, R. D. Frostig, R. M. Siegel, and E. Bartfeld, “High-resolution optical imaging of functional brain architecture in the awake monkey,” Proc. Natl. Acad. Sci. U.S.A. 88(24), 11559–11563 (1991).
    [Crossref] [PubMed]
  3. D. Malonek and A. Grinvald, “Interactions between electrical activity and cortical microcirculation revealed by imaging spectroscopy: implications for functional brain mapping,” Science 272(5261), 551–554 (1996).
    [Crossref] [PubMed]
  4. R. S. Menon, S. Ogawa, X. Hu, J. P. Strupp, P. Anderson, and K. Uğurbil, “BOLD based functional MRI at 4 Tesla includes a capillary bed contribution: echo-planar imaging correlates with previous optical imaging using intrinsic signals,” Magn. Reson. Med. 33(3), 453–459 (1995).
    [Crossref] [PubMed]
  5. E. Yacoub and X. Hu, “Detection of the early negative response in fMRI at 1.5 Tesla,” Magn. Reson. Med. 41(6), 1088–1092 (1999).
    [Crossref] [PubMed]
  6. T. Q. Duong, D. S. Kim, K. Uğurbil, and S. G. Kim, “Spatiotemporal dynamics of the BOLD fMRI signals: toward mapping submillimeter cortical columns using the early negative response,” Magn. Reson. Med. 44(2), 231–242 (2000).
    [Crossref] [PubMed]
  7. X. Hu and E. Yacoub, “The story of the initial dip in fMRI,” Neuroimage 62(2), 1103–1108 (2012).
    [Crossref] [PubMed]
  8. R. B. Buxton, E. C. Wong, and L. R. Frank, “Dynamics of blood flow and oxygenation changes during brain activation: the balloon model,” Magn. Reson. Med. 39(6), 855–864 (1998).
    [Crossref] [PubMed]
  9. R. B. Buxton, K. Uludağ, D. J. Dubowitz, and T. T. Liu, “Modeling the hemodynamic response to brain activation,” Neuroimage 23(Suppl 1), S220–S233 (2004).
    [Crossref] [PubMed]
  10. J. B. Mandeville, J. J. Marota, C. Ayata, M. A. Moskowitz, R. M. Weisskoff, and B. R. Rosen, “MRI measurement of the temporal evolution of relative CMRO2 during rat forepaw stimulation,” Magn. Reson. Med. 42(5), 944–951 (1999).
    [Crossref] [PubMed]
  11. G. Jasdzewski, G. Strangman, J. Wagner, K. K. Kwong, R. A. Poldrack, and D. A. Boas, “Differences in the hemodynamic response to event-related motor and visual paradigms as measured by near-infrared spectroscopy,” Neuroimage 20(1), 479–488 (2003).
    [Crossref] [PubMed]
  12. T. Kato, “Principle and technique of NIRS imaging for human brain FORCE: fast-oxygen response in capillary event,” Proc. ISBET1270, 85–90 (2004).
    [Crossref]
  13. N. K. Logothetis, H. Guggenberger, S. Peled, and J. Pauls, “Functional imaging of the monkey brain,” Nat. Neurosci. 2(6), 555–562 (1999).
    [Crossref] [PubMed]
  14. M. Jones, J. Berwick, D. Johnston, and J. Mayhew, “Concurrent optical imaging spectroscopy and laser-Doppler flowmetry: the relationship between blood flow, oxygenation, and volume in rodent barrel cortex,” Neuroimage 13(6), 1002–1015 (2001).
    [Crossref] [PubMed]
  15. X. Hu, T. H. Le, and K. Uğurbil, “Evaluation of the early response in fMRI in individual subjects using short stimulus duration,” Magn. Reson. Med. 37(6), 877–884 (1997).
    [Crossref] [PubMed]
  16. T. Ernst and J. Hennig, “Observation of a fast response in functional MR,” Magn. Reson. Med. 32(1), 146–149 (1994).
    [Crossref] [PubMed]
  17. D. S. Kim, T. Q. Duong, and S. G. Kim, “High-resolution mapping of iso-orientation columns by fMRI,” Nat. Neurosci. 3(2), 164–169 (2000).
    [Crossref] [PubMed]
  18. M. Watanabe, A. Bartels, J. H. Macke, Y. Murayama, and N. K. Logothetis, “Temporal jitter of the BOLD signal reveals a reliable initial dip and improved spatial resolution,” Curr. Biol. 23(21), 2146–2150 (2013).
    [Crossref] [PubMed]
  19. M. Ferrari and V. Quaresima, “A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application,” Neuroimage 63(2), 921–935 (2012).
    [Crossref] [PubMed]
  20. N. Naseer and K.-S. Hong, “fNIRS-based brain-computer interfaces: a review,” Front. Hum. Neurosci. 9, 3 (2015).
    [PubMed]
  21. D. A. Boas, C. E. Elwell, M. Ferrari, and G. Taga, “Twenty years of functional near-infrared spectroscopy: Introduction for the special issue,” Neuroimage 85(Pt 1), 1–5 (2014).
    [Crossref] [PubMed]
  22. M. R. Bhutta, K.-S. Hong, B.-M. Kim, M. J. Hong, Y.-H. Kim, and S.-H. Lee, “Note: three wavelengths near-infrared spectroscopy system for compensating the light absorbance by water,” Rev. Sci. Instrum. 85(2), 026111 (2014).
    [Crossref] [PubMed]
  23. H. Santosa, M. J. Hong, S.-P. Kim, and K.-S. Hong, “Noise reduction in functional near-infrared spectroscopy signals by independent component analysis,” Rev. Sci. Instrum. 84(7), 073106 (2013).
    [Crossref] [PubMed]
  24. N. Liu, X. Cui, D. M. Bryant, G. H. Glover, and A. L. Reiss, “Inferring deep-brain activity from cortical activity using functional near-infrared spectroscopy,” Biomed. Opt. Express 6(3), 1074–1089 (2015).
    [Crossref] [PubMed]
  25. T. Akiyama, T. Ohira, T. Kawase, and T. Kato, “TMS orientation for NIRS-functional motor mapping,” Brain Topogr. 19(1-2), 1–9 (2006).
    [Crossref] [PubMed]
  26. G. R. Wylie, H. L. Graber, G. T. Voelbel, A. D. Kohl, J. DeLuca, Y. Pei, Y. Xu, and R. L. Barbour, “Using co-variations in the Hb signal to detect visual activation: a near infrared spectroscopic imaging study,” Neuroimage 47(2), 473–481 (2009).
    [Crossref] [PubMed]
  27. K. Yoshino and T. Kato, “Vector-based phase classification of initial dips during word listening using near-infrared spectroscopy,” Neuroreport 23(16), 947–951 (2012).
    [Crossref] [PubMed]
  28. N. Oka, K. Yoshino, K. Yamamoto, H. Takahashi, S. Li, T. Sugimachi, K. Nakano, Y. Suda, and T. Kato, “Greater activity in the frontal cortex on left curves: a vector-based fNIRS study of left and right curve driving,” PLoS One 10(5), e0127594 (2015).
    [Crossref] [PubMed]
  29. K. Yoshino, N. Oka, K. Yamamoto, H. Takahashi, and T. Kato, “Correlation of prefrontal cortical activation with changing vehicle speeds in actual driving: a vector-based functional near-infrared spectroscopy study,” Front. Hum. Neurosci. 7, 895 (2013).
    [Crossref] [PubMed]
  30. K.-S. Hong and N. Naseer, “Reduction of delay in detecting initial dips from functional near-infrared spectroscopy signals using vector-based phase analysis,” Int. J. Neural Syst. 26(3), 1650012 (2016).
    [Crossref] [PubMed]
  31. F. Matthews, B. A. Pearlmutter, T. E. Ward, C. Soraghan, and C. Markham, “Hemodynamics for brain computer interfaces,” IEEE Signal Process. Mag. 25(1), 87–94 (2008).
    [Crossref]
  32. J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “Brain-computer interfaces for communication and control,” Clin. Neurophysiol. 113(6), 767–791 (2002).
    [Crossref] [PubMed]
  33. D. J. McFarland and J. R. Wolpaw, “Brain-computer interfaces for the operation of robotic and prosthetic devices,” Adv. Comput. 79, 169–187 (2010).
    [Crossref]
  34. L. F. Nicolas-Alonso and J. Gomez-Gil, “Brain computer interfaces, a review,” Sensors (Basel) 12(12), 1211–1279 (2012).
    [Crossref] [PubMed]
  35. A. Ortiz-Rosario and H. Adeli, “Brain-computer interface technologies: from signal to action,” Rev. Neurosci. 24(5), 537–552 (2013).
    [Crossref] [PubMed]
  36. K.-S. Hong and H.-D. Nguyen, “State-space models of impulse hemodynamic responses over motor, somatosensory, and visual cortices,” Biomed. Opt. Express 5(6), 1778–1798 (2014).
    [Crossref] [PubMed]
  37. S. D. Power, A. Kushki, and T. Chau, “Towards a system-paced near-infrared spectroscopy brain-computer interface: differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state,” J. Neural Eng. 8(6), 066004 (2011).
    [Crossref] [PubMed]
  38. S. D. Power, A. Kushki, and T. Chau, “Automatic single-trial discrimination of mental arithmetic, mental singing and the no-control state from prefrontal activity: toward a three-state NIRS-BCI,” BMC Res. Notes 5(1), 141 (2012).
    [Crossref] [PubMed]
  39. H. Santosa, M. J. Hong, and K.-S. Hong, “Lateralization of music processing with noise in the auditory cortex: an fNIRS study,” Front. Behav. Neurosci. 8, 418 (2014).
  40. M. J. Khan and K.-S. Hong, “Passive BCI based on drowsiness detection: an fNIRS study,” Biomed. Opt. Express 6(10), 4063–4078 (2015).
    [Crossref] [PubMed]
  41. N. Naseer, M. J. Hong, and K.-S. Hong, “Online binary decision decoding using functional near-infrared spectroscopy for the development of brain-computer interface,” Exp. Brain Res. 232(2), 555–564 (2014).
    [Crossref] [PubMed]
  42. M. J. Khan, M. J. Hong, and K.-S. Hong, “Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface,” Front. Hum. Neurosci. 8, 244 (2014).
    [Crossref] [PubMed]
  43. L. C. Schudlo and T. Chau, “Dynamic topographical pattern classification of multichannel prefrontal NIRS signals: II. Online differentiation of mental arithmetic and rest,” J. Neural Eng. 11(1), 016003 (2014).
    [Crossref] [PubMed]
  44. M. R. Bhutta, M. J. Hong, Y.-H. Kim, and K.-S. Hong, “Single-trial lie detection using a combined fNIRS-polygraph system,” Front. Psychol. 6, 709 (2015).
    [Crossref] [PubMed]
  45. K.-S. Hong and H. Santosa, “Decoding four different sound-categories in the auditory cortex using functional near-infrared spectroscopy,” Hear. Res. 333, 157–166 (2016).
    [Crossref] [PubMed]
  46. K.-S. Hong, N. Naseer, and Y.-H. Kim, “Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI,” Neurosci. Lett. 587, 87–92 (2015).
    [Crossref] [PubMed]
  47. T. Gateau, G. Durantin, F. Lancelot, S. Scannella, and F. Dehais, “Real-time state estimation in a flight simulator using fNIRS,” PLoS One 10(3), e0121279 (2015).
    [Crossref] [PubMed]
  48. B. Christie, “Doctors revise declaration of Helsinki,” BMJ 321(7266), 913 (2000).
    [Crossref] [PubMed]
  49. N. Naseer, F. M. Noori, N. K. Qureshi, and K.-S. Hong, “Determining optimal feature-combination for LDA classification of functional near-infrared spectroscopy signals in brain-computer interface application,” Front. Hum. Neurosci. 10, 237 (2016).
    [Crossref] [PubMed]
  50. S. Weyand, K. Takehara-Nishiuchi, and T. Chau, “Weaning off mental tasks to achieve voluntary self-regulatory control of a near-infrared spectroscopy brain-computer interface,” IEEE Trans. Neural Syst. Rehabil. Eng. 23(4), 548–561 (2015).
    [Crossref] [PubMed]
  51. N. Naseer and K.-S. Hong, “Decoding answers to four-choice questions using functional near infrared spectroscopy,” J. Near Infrared Spectrosc. 23(1), 23–31 (2015).
    [Crossref]
  52. M. Naito, Y. Michioka, K. Ozawa, Y. Ito, M. Kiguchi, and T. Kanazawa, “A communication means for totally locked-in ALS patients based on changes in cerebral blood volume measured with near-infrared light,” IEICE Trans. Inf. Syst. E90-D(7), 1028–1037 (2007).
    [Crossref]
  53. H. Ayaz, P. A. Shewokis, M. Izzetoğlu, M. P. Čakır, and B. Onaral, “Tangram solved? Prefrontal cortex activation analysis during geometric problem solving,” in Proceedings of IEEE Engineering in Medicine and Biology Society (IEEE, 2013), 4724–4727.
  54. D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of optical pathlength through tissue from direct time of flight measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988).
    [Crossref] [PubMed]
  55. R. Zimmermann, L. Marchal-Crespo, J. Edelmann, O. Lambercy, M. C. Fluet, R. Riener, M. Wolf, and R. Gassert, “Detection of motor execution using a hybrid fNIRS-biosignal BCI: a feasibility study,” J. Neuroeng. Rehabil. 10(1), 4 (2013).
    [Crossref] [PubMed]
  56. Q. Zhang, E. N. Brown, and G. E. Strangman, “Adaptive filtering to reduce global interference in evoked brain activity detection: a human subject case study,” J. Biomed. Opt. 12(6), 064009 (2007).
    [Crossref] [PubMed]
  57. T. Fawcett, “An introduction to ROC analysis,” Pattern Recognit. Lett. 27(8), 861–874 (2006).
    [Crossref]
  58. H.-D. Nguyen, K. S. Hong, and Y. I. Shin, “Bundled optode method in functional near-infrared spectroscopy,” PLoS One 11(10), e0165146 (2016).
    [Crossref] [PubMed]
  59. N. Naseer and K.-S. Hong, “Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain-computer interface,” Neurosci. Lett. 553, 84–89 (2013).
    [Crossref] [PubMed]
  60. C. Herff, D. Heger, O. Fortmann, J. Hennrich, F. Putze, and T. Schultz, “Mental workload during n-back task-quantified in the prefrontal cortex using fNIRS,” Front. Hum. Neurosci. 7, 935 (2014).
    [Crossref] [PubMed]
  61. L. C Schudlo and T. Chau, “Towards a ternary NIRS-BCI: single-trial classification of verbal fluency task, Stroop task and unconstrained rest,” J. Neural Eng. 12(6), 066008 (2015).
    [Crossref] [PubMed]
  62. Y. Behzadi and T. T. Liu, “Caffeine reduces the initial dip in the visual BOLD response at 3 T,” Neuroimage 32(1), 9–15 (2006).
    [Crossref] [PubMed]
  63. R. B. Buxton, “The elusive initial dip,” Neuroimage 13(6), 953–958 (2001).
    [Crossref] [PubMed]
  64. X.-S. Hu, K.-S. Hong, and S. S. Ge, “Reduction of trial-to-trial variability in functional near-infrared spectroscopy signals by accounting for resting-state functional connectivity,” J. Biomed. Opt. 18(1), 017003 (2013).
    [Crossref] [PubMed]
  65. E. Yacoub and X. Hu, “Detection of the early decrease in fMRI signal in the motor area,” Magn. Reson. Med. 45(2), 184–190 (2001).
    [Crossref] [PubMed]

2016 (4)

K.-S. Hong and N. Naseer, “Reduction of delay in detecting initial dips from functional near-infrared spectroscopy signals using vector-based phase analysis,” Int. J. Neural Syst. 26(3), 1650012 (2016).
[Crossref] [PubMed]

K.-S. Hong and H. Santosa, “Decoding four different sound-categories in the auditory cortex using functional near-infrared spectroscopy,” Hear. Res. 333, 157–166 (2016).
[Crossref] [PubMed]

N. Naseer, F. M. Noori, N. K. Qureshi, and K.-S. Hong, “Determining optimal feature-combination for LDA classification of functional near-infrared spectroscopy signals in brain-computer interface application,” Front. Hum. Neurosci. 10, 237 (2016).
[Crossref] [PubMed]

H.-D. Nguyen, K. S. Hong, and Y. I. Shin, “Bundled optode method in functional near-infrared spectroscopy,” PLoS One 11(10), e0165146 (2016).
[Crossref] [PubMed]

2015 (10)

M. R. Bhutta, M. J. Hong, Y.-H. Kim, and K.-S. Hong, “Single-trial lie detection using a combined fNIRS-polygraph system,” Front. Psychol. 6, 709 (2015).
[Crossref] [PubMed]

L. C Schudlo and T. Chau, “Towards a ternary NIRS-BCI: single-trial classification of verbal fluency task, Stroop task and unconstrained rest,” J. Neural Eng. 12(6), 066008 (2015).
[Crossref] [PubMed]

S. Weyand, K. Takehara-Nishiuchi, and T. Chau, “Weaning off mental tasks to achieve voluntary self-regulatory control of a near-infrared spectroscopy brain-computer interface,” IEEE Trans. Neural Syst. Rehabil. Eng. 23(4), 548–561 (2015).
[Crossref] [PubMed]

N. Naseer and K.-S. Hong, “Decoding answers to four-choice questions using functional near infrared spectroscopy,” J. Near Infrared Spectrosc. 23(1), 23–31 (2015).
[Crossref]

K.-S. Hong, N. Naseer, and Y.-H. Kim, “Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI,” Neurosci. Lett. 587, 87–92 (2015).
[Crossref] [PubMed]

T. Gateau, G. Durantin, F. Lancelot, S. Scannella, and F. Dehais, “Real-time state estimation in a flight simulator using fNIRS,” PLoS One 10(3), e0121279 (2015).
[Crossref] [PubMed]

M. J. Khan and K.-S. Hong, “Passive BCI based on drowsiness detection: an fNIRS study,” Biomed. Opt. Express 6(10), 4063–4078 (2015).
[Crossref] [PubMed]

N. Oka, K. Yoshino, K. Yamamoto, H. Takahashi, S. Li, T. Sugimachi, K. Nakano, Y. Suda, and T. Kato, “Greater activity in the frontal cortex on left curves: a vector-based fNIRS study of left and right curve driving,” PLoS One 10(5), e0127594 (2015).
[Crossref] [PubMed]

N. Naseer and K.-S. Hong, “fNIRS-based brain-computer interfaces: a review,” Front. Hum. Neurosci. 9, 3 (2015).
[PubMed]

N. Liu, X. Cui, D. M. Bryant, G. H. Glover, and A. L. Reiss, “Inferring deep-brain activity from cortical activity using functional near-infrared spectroscopy,” Biomed. Opt. Express 6(3), 1074–1089 (2015).
[Crossref] [PubMed]

2014 (8)

D. A. Boas, C. E. Elwell, M. Ferrari, and G. Taga, “Twenty years of functional near-infrared spectroscopy: Introduction for the special issue,” Neuroimage 85(Pt 1), 1–5 (2014).
[Crossref] [PubMed]

M. R. Bhutta, K.-S. Hong, B.-M. Kim, M. J. Hong, Y.-H. Kim, and S.-H. Lee, “Note: three wavelengths near-infrared spectroscopy system for compensating the light absorbance by water,” Rev. Sci. Instrum. 85(2), 026111 (2014).
[Crossref] [PubMed]

N. Naseer, M. J. Hong, and K.-S. Hong, “Online binary decision decoding using functional near-infrared spectroscopy for the development of brain-computer interface,” Exp. Brain Res. 232(2), 555–564 (2014).
[Crossref] [PubMed]

M. J. Khan, M. J. Hong, and K.-S. Hong, “Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface,” Front. Hum. Neurosci. 8, 244 (2014).
[Crossref] [PubMed]

L. C. Schudlo and T. Chau, “Dynamic topographical pattern classification of multichannel prefrontal NIRS signals: II. Online differentiation of mental arithmetic and rest,” J. Neural Eng. 11(1), 016003 (2014).
[Crossref] [PubMed]

K.-S. Hong and H.-D. Nguyen, “State-space models of impulse hemodynamic responses over motor, somatosensory, and visual cortices,” Biomed. Opt. Express 5(6), 1778–1798 (2014).
[Crossref] [PubMed]

H. Santosa, M. J. Hong, and K.-S. Hong, “Lateralization of music processing with noise in the auditory cortex: an fNIRS study,” Front. Behav. Neurosci. 8, 418 (2014).

C. Herff, D. Heger, O. Fortmann, J. Hennrich, F. Putze, and T. Schultz, “Mental workload during n-back task-quantified in the prefrontal cortex using fNIRS,” Front. Hum. Neurosci. 7, 935 (2014).
[Crossref] [PubMed]

2013 (7)

N. Naseer and K.-S. Hong, “Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain-computer interface,” Neurosci. Lett. 553, 84–89 (2013).
[Crossref] [PubMed]

R. Zimmermann, L. Marchal-Crespo, J. Edelmann, O. Lambercy, M. C. Fluet, R. Riener, M. Wolf, and R. Gassert, “Detection of motor execution using a hybrid fNIRS-biosignal BCI: a feasibility study,” J. Neuroeng. Rehabil. 10(1), 4 (2013).
[Crossref] [PubMed]

X.-S. Hu, K.-S. Hong, and S. S. Ge, “Reduction of trial-to-trial variability in functional near-infrared spectroscopy signals by accounting for resting-state functional connectivity,” J. Biomed. Opt. 18(1), 017003 (2013).
[Crossref] [PubMed]

H. Santosa, M. J. Hong, S.-P. Kim, and K.-S. Hong, “Noise reduction in functional near-infrared spectroscopy signals by independent component analysis,” Rev. Sci. Instrum. 84(7), 073106 (2013).
[Crossref] [PubMed]

K. Yoshino, N. Oka, K. Yamamoto, H. Takahashi, and T. Kato, “Correlation of prefrontal cortical activation with changing vehicle speeds in actual driving: a vector-based functional near-infrared spectroscopy study,” Front. Hum. Neurosci. 7, 895 (2013).
[Crossref] [PubMed]

A. Ortiz-Rosario and H. Adeli, “Brain-computer interface technologies: from signal to action,” Rev. Neurosci. 24(5), 537–552 (2013).
[Crossref] [PubMed]

M. Watanabe, A. Bartels, J. H. Macke, Y. Murayama, and N. K. Logothetis, “Temporal jitter of the BOLD signal reveals a reliable initial dip and improved spatial resolution,” Curr. Biol. 23(21), 2146–2150 (2013).
[Crossref] [PubMed]

2012 (5)

M. Ferrari and V. Quaresima, “A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application,” Neuroimage 63(2), 921–935 (2012).
[Crossref] [PubMed]

X. Hu and E. Yacoub, “The story of the initial dip in fMRI,” Neuroimage 62(2), 1103–1108 (2012).
[Crossref] [PubMed]

L. F. Nicolas-Alonso and J. Gomez-Gil, “Brain computer interfaces, a review,” Sensors (Basel) 12(12), 1211–1279 (2012).
[Crossref] [PubMed]

K. Yoshino and T. Kato, “Vector-based phase classification of initial dips during word listening using near-infrared spectroscopy,” Neuroreport 23(16), 947–951 (2012).
[Crossref] [PubMed]

S. D. Power, A. Kushki, and T. Chau, “Automatic single-trial discrimination of mental arithmetic, mental singing and the no-control state from prefrontal activity: toward a three-state NIRS-BCI,” BMC Res. Notes 5(1), 141 (2012).
[Crossref] [PubMed]

2011 (1)

S. D. Power, A. Kushki, and T. Chau, “Towards a system-paced near-infrared spectroscopy brain-computer interface: differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state,” J. Neural Eng. 8(6), 066004 (2011).
[Crossref] [PubMed]

2010 (1)

D. J. McFarland and J. R. Wolpaw, “Brain-computer interfaces for the operation of robotic and prosthetic devices,” Adv. Comput. 79, 169–187 (2010).
[Crossref]

2009 (1)

G. R. Wylie, H. L. Graber, G. T. Voelbel, A. D. Kohl, J. DeLuca, Y. Pei, Y. Xu, and R. L. Barbour, “Using co-variations in the Hb signal to detect visual activation: a near infrared spectroscopic imaging study,” Neuroimage 47(2), 473–481 (2009).
[Crossref] [PubMed]

2008 (1)

F. Matthews, B. A. Pearlmutter, T. E. Ward, C. Soraghan, and C. Markham, “Hemodynamics for brain computer interfaces,” IEEE Signal Process. Mag. 25(1), 87–94 (2008).
[Crossref]

2007 (2)

M. Naito, Y. Michioka, K. Ozawa, Y. Ito, M. Kiguchi, and T. Kanazawa, “A communication means for totally locked-in ALS patients based on changes in cerebral blood volume measured with near-infrared light,” IEICE Trans. Inf. Syst. E90-D(7), 1028–1037 (2007).
[Crossref]

Q. Zhang, E. N. Brown, and G. E. Strangman, “Adaptive filtering to reduce global interference in evoked brain activity detection: a human subject case study,” J. Biomed. Opt. 12(6), 064009 (2007).
[Crossref] [PubMed]

2006 (3)

T. Fawcett, “An introduction to ROC analysis,” Pattern Recognit. Lett. 27(8), 861–874 (2006).
[Crossref]

Y. Behzadi and T. T. Liu, “Caffeine reduces the initial dip in the visual BOLD response at 3 T,” Neuroimage 32(1), 9–15 (2006).
[Crossref] [PubMed]

T. Akiyama, T. Ohira, T. Kawase, and T. Kato, “TMS orientation for NIRS-functional motor mapping,” Brain Topogr. 19(1-2), 1–9 (2006).
[Crossref] [PubMed]

2004 (1)

R. B. Buxton, K. Uludağ, D. J. Dubowitz, and T. T. Liu, “Modeling the hemodynamic response to brain activation,” Neuroimage 23(Suppl 1), S220–S233 (2004).
[Crossref] [PubMed]

2003 (1)

G. Jasdzewski, G. Strangman, J. Wagner, K. K. Kwong, R. A. Poldrack, and D. A. Boas, “Differences in the hemodynamic response to event-related motor and visual paradigms as measured by near-infrared spectroscopy,” Neuroimage 20(1), 479–488 (2003).
[Crossref] [PubMed]

2002 (1)

J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “Brain-computer interfaces for communication and control,” Clin. Neurophysiol. 113(6), 767–791 (2002).
[Crossref] [PubMed]

2001 (3)

M. Jones, J. Berwick, D. Johnston, and J. Mayhew, “Concurrent optical imaging spectroscopy and laser-Doppler flowmetry: the relationship between blood flow, oxygenation, and volume in rodent barrel cortex,” Neuroimage 13(6), 1002–1015 (2001).
[Crossref] [PubMed]

R. B. Buxton, “The elusive initial dip,” Neuroimage 13(6), 953–958 (2001).
[Crossref] [PubMed]

E. Yacoub and X. Hu, “Detection of the early decrease in fMRI signal in the motor area,” Magn. Reson. Med. 45(2), 184–190 (2001).
[Crossref] [PubMed]

2000 (3)

B. Christie, “Doctors revise declaration of Helsinki,” BMJ 321(7266), 913 (2000).
[Crossref] [PubMed]

T. Q. Duong, D. S. Kim, K. Uğurbil, and S. G. Kim, “Spatiotemporal dynamics of the BOLD fMRI signals: toward mapping submillimeter cortical columns using the early negative response,” Magn. Reson. Med. 44(2), 231–242 (2000).
[Crossref] [PubMed]

D. S. Kim, T. Q. Duong, and S. G. Kim, “High-resolution mapping of iso-orientation columns by fMRI,” Nat. Neurosci. 3(2), 164–169 (2000).
[Crossref] [PubMed]

1999 (3)

E. Yacoub and X. Hu, “Detection of the early negative response in fMRI at 1.5 Tesla,” Magn. Reson. Med. 41(6), 1088–1092 (1999).
[Crossref] [PubMed]

N. K. Logothetis, H. Guggenberger, S. Peled, and J. Pauls, “Functional imaging of the monkey brain,” Nat. Neurosci. 2(6), 555–562 (1999).
[Crossref] [PubMed]

J. B. Mandeville, J. J. Marota, C. Ayata, M. A. Moskowitz, R. M. Weisskoff, and B. R. Rosen, “MRI measurement of the temporal evolution of relative CMRO2 during rat forepaw stimulation,” Magn. Reson. Med. 42(5), 944–951 (1999).
[Crossref] [PubMed]

1998 (1)

R. B. Buxton, E. C. Wong, and L. R. Frank, “Dynamics of blood flow and oxygenation changes during brain activation: the balloon model,” Magn. Reson. Med. 39(6), 855–864 (1998).
[Crossref] [PubMed]

1997 (1)

X. Hu, T. H. Le, and K. Uğurbil, “Evaluation of the early response in fMRI in individual subjects using short stimulus duration,” Magn. Reson. Med. 37(6), 877–884 (1997).
[Crossref] [PubMed]

1996 (1)

D. Malonek and A. Grinvald, “Interactions between electrical activity and cortical microcirculation revealed by imaging spectroscopy: implications for functional brain mapping,” Science 272(5261), 551–554 (1996).
[Crossref] [PubMed]

1995 (1)

R. S. Menon, S. Ogawa, X. Hu, J. P. Strupp, P. Anderson, and K. Uğurbil, “BOLD based functional MRI at 4 Tesla includes a capillary bed contribution: echo-planar imaging correlates with previous optical imaging using intrinsic signals,” Magn. Reson. Med. 33(3), 453–459 (1995).
[Crossref] [PubMed]

1994 (1)

T. Ernst and J. Hennig, “Observation of a fast response in functional MR,” Magn. Reson. Med. 32(1), 146–149 (1994).
[Crossref] [PubMed]

1991 (1)

A. Grinvald, R. D. Frostig, R. M. Siegel, and E. Bartfeld, “High-resolution optical imaging of functional brain architecture in the awake monkey,” Proc. Natl. Acad. Sci. U.S.A. 88(24), 11559–11563 (1991).
[Crossref] [PubMed]

1990 (1)

R. D. Frostig, E. E. Lieke, D. Y. Ts’o, and A. Grinvald, “Cortical functional architecture and local coupling between neuronal activity and the microcirculation revealed by in vivo high-resolution optical imaging of intrinsic signals,” Proc. Natl. Acad. Sci. U.S.A. 87(16), 6082–6086 (1990).
[Crossref] [PubMed]

1988 (1)

D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of optical pathlength through tissue from direct time of flight measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988).
[Crossref] [PubMed]

Adeli, H.

A. Ortiz-Rosario and H. Adeli, “Brain-computer interface technologies: from signal to action,” Rev. Neurosci. 24(5), 537–552 (2013).
[Crossref] [PubMed]

Akiyama, T.

T. Akiyama, T. Ohira, T. Kawase, and T. Kato, “TMS orientation for NIRS-functional motor mapping,” Brain Topogr. 19(1-2), 1–9 (2006).
[Crossref] [PubMed]

Anderson, P.

R. S. Menon, S. Ogawa, X. Hu, J. P. Strupp, P. Anderson, and K. Uğurbil, “BOLD based functional MRI at 4 Tesla includes a capillary bed contribution: echo-planar imaging correlates with previous optical imaging using intrinsic signals,” Magn. Reson. Med. 33(3), 453–459 (1995).
[Crossref] [PubMed]

Arridge, S.

D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of optical pathlength through tissue from direct time of flight measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988).
[Crossref] [PubMed]

Ayata, C.

J. B. Mandeville, J. J. Marota, C. Ayata, M. A. Moskowitz, R. M. Weisskoff, and B. R. Rosen, “MRI measurement of the temporal evolution of relative CMRO2 during rat forepaw stimulation,” Magn. Reson. Med. 42(5), 944–951 (1999).
[Crossref] [PubMed]

Ayaz, H.

H. Ayaz, P. A. Shewokis, M. Izzetoğlu, M. P. Čakır, and B. Onaral, “Tangram solved? Prefrontal cortex activation analysis during geometric problem solving,” in Proceedings of IEEE Engineering in Medicine and Biology Society (IEEE, 2013), 4724–4727.

Barbour, R. L.

G. R. Wylie, H. L. Graber, G. T. Voelbel, A. D. Kohl, J. DeLuca, Y. Pei, Y. Xu, and R. L. Barbour, “Using co-variations in the Hb signal to detect visual activation: a near infrared spectroscopic imaging study,” Neuroimage 47(2), 473–481 (2009).
[Crossref] [PubMed]

Bartels, A.

M. Watanabe, A. Bartels, J. H. Macke, Y. Murayama, and N. K. Logothetis, “Temporal jitter of the BOLD signal reveals a reliable initial dip and improved spatial resolution,” Curr. Biol. 23(21), 2146–2150 (2013).
[Crossref] [PubMed]

Bartfeld, E.

A. Grinvald, R. D. Frostig, R. M. Siegel, and E. Bartfeld, “High-resolution optical imaging of functional brain architecture in the awake monkey,” Proc. Natl. Acad. Sci. U.S.A. 88(24), 11559–11563 (1991).
[Crossref] [PubMed]

Behzadi, Y.

Y. Behzadi and T. T. Liu, “Caffeine reduces the initial dip in the visual BOLD response at 3 T,” Neuroimage 32(1), 9–15 (2006).
[Crossref] [PubMed]

Berwick, J.

M. Jones, J. Berwick, D. Johnston, and J. Mayhew, “Concurrent optical imaging spectroscopy and laser-Doppler flowmetry: the relationship between blood flow, oxygenation, and volume in rodent barrel cortex,” Neuroimage 13(6), 1002–1015 (2001).
[Crossref] [PubMed]

Bhutta, M. R.

M. R. Bhutta, M. J. Hong, Y.-H. Kim, and K.-S. Hong, “Single-trial lie detection using a combined fNIRS-polygraph system,” Front. Psychol. 6, 709 (2015).
[Crossref] [PubMed]

M. R. Bhutta, K.-S. Hong, B.-M. Kim, M. J. Hong, Y.-H. Kim, and S.-H. Lee, “Note: three wavelengths near-infrared spectroscopy system for compensating the light absorbance by water,” Rev. Sci. Instrum. 85(2), 026111 (2014).
[Crossref] [PubMed]

Birbaumer, N.

J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “Brain-computer interfaces for communication and control,” Clin. Neurophysiol. 113(6), 767–791 (2002).
[Crossref] [PubMed]

Boas, D. A.

D. A. Boas, C. E. Elwell, M. Ferrari, and G. Taga, “Twenty years of functional near-infrared spectroscopy: Introduction for the special issue,” Neuroimage 85(Pt 1), 1–5 (2014).
[Crossref] [PubMed]

G. Jasdzewski, G. Strangman, J. Wagner, K. K. Kwong, R. A. Poldrack, and D. A. Boas, “Differences in the hemodynamic response to event-related motor and visual paradigms as measured by near-infrared spectroscopy,” Neuroimage 20(1), 479–488 (2003).
[Crossref] [PubMed]

Brown, E. N.

Q. Zhang, E. N. Brown, and G. E. Strangman, “Adaptive filtering to reduce global interference in evoked brain activity detection: a human subject case study,” J. Biomed. Opt. 12(6), 064009 (2007).
[Crossref] [PubMed]

Bryant, D. M.

Buxton, R. B.

R. B. Buxton, K. Uludağ, D. J. Dubowitz, and T. T. Liu, “Modeling the hemodynamic response to brain activation,” Neuroimage 23(Suppl 1), S220–S233 (2004).
[Crossref] [PubMed]

R. B. Buxton, “The elusive initial dip,” Neuroimage 13(6), 953–958 (2001).
[Crossref] [PubMed]

R. B. Buxton, E. C. Wong, and L. R. Frank, “Dynamics of blood flow and oxygenation changes during brain activation: the balloon model,” Magn. Reson. Med. 39(6), 855–864 (1998).
[Crossref] [PubMed]

C Schudlo, L.

L. C Schudlo and T. Chau, “Towards a ternary NIRS-BCI: single-trial classification of verbal fluency task, Stroop task and unconstrained rest,” J. Neural Eng. 12(6), 066008 (2015).
[Crossref] [PubMed]

Cakir, M. P.

H. Ayaz, P. A. Shewokis, M. Izzetoğlu, M. P. Čakır, and B. Onaral, “Tangram solved? Prefrontal cortex activation analysis during geometric problem solving,” in Proceedings of IEEE Engineering in Medicine and Biology Society (IEEE, 2013), 4724–4727.

Chau, T.

S. Weyand, K. Takehara-Nishiuchi, and T. Chau, “Weaning off mental tasks to achieve voluntary self-regulatory control of a near-infrared spectroscopy brain-computer interface,” IEEE Trans. Neural Syst. Rehabil. Eng. 23(4), 548–561 (2015).
[Crossref] [PubMed]

L. C Schudlo and T. Chau, “Towards a ternary NIRS-BCI: single-trial classification of verbal fluency task, Stroop task and unconstrained rest,” J. Neural Eng. 12(6), 066008 (2015).
[Crossref] [PubMed]

L. C. Schudlo and T. Chau, “Dynamic topographical pattern classification of multichannel prefrontal NIRS signals: II. Online differentiation of mental arithmetic and rest,” J. Neural Eng. 11(1), 016003 (2014).
[Crossref] [PubMed]

S. D. Power, A. Kushki, and T. Chau, “Automatic single-trial discrimination of mental arithmetic, mental singing and the no-control state from prefrontal activity: toward a three-state NIRS-BCI,” BMC Res. Notes 5(1), 141 (2012).
[Crossref] [PubMed]

S. D. Power, A. Kushki, and T. Chau, “Towards a system-paced near-infrared spectroscopy brain-computer interface: differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state,” J. Neural Eng. 8(6), 066004 (2011).
[Crossref] [PubMed]

Christie, B.

B. Christie, “Doctors revise declaration of Helsinki,” BMJ 321(7266), 913 (2000).
[Crossref] [PubMed]

Cope, M.

D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of optical pathlength through tissue from direct time of flight measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988).
[Crossref] [PubMed]

Cui, X.

Dehais, F.

T. Gateau, G. Durantin, F. Lancelot, S. Scannella, and F. Dehais, “Real-time state estimation in a flight simulator using fNIRS,” PLoS One 10(3), e0121279 (2015).
[Crossref] [PubMed]

Delpy, D. T.

D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of optical pathlength through tissue from direct time of flight measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988).
[Crossref] [PubMed]

DeLuca, J.

G. R. Wylie, H. L. Graber, G. T. Voelbel, A. D. Kohl, J. DeLuca, Y. Pei, Y. Xu, and R. L. Barbour, “Using co-variations in the Hb signal to detect visual activation: a near infrared spectroscopic imaging study,” Neuroimage 47(2), 473–481 (2009).
[Crossref] [PubMed]

Dubowitz, D. J.

R. B. Buxton, K. Uludağ, D. J. Dubowitz, and T. T. Liu, “Modeling the hemodynamic response to brain activation,” Neuroimage 23(Suppl 1), S220–S233 (2004).
[Crossref] [PubMed]

Duong, T. Q.

T. Q. Duong, D. S. Kim, K. Uğurbil, and S. G. Kim, “Spatiotemporal dynamics of the BOLD fMRI signals: toward mapping submillimeter cortical columns using the early negative response,” Magn. Reson. Med. 44(2), 231–242 (2000).
[Crossref] [PubMed]

D. S. Kim, T. Q. Duong, and S. G. Kim, “High-resolution mapping of iso-orientation columns by fMRI,” Nat. Neurosci. 3(2), 164–169 (2000).
[Crossref] [PubMed]

Durantin, G.

T. Gateau, G. Durantin, F. Lancelot, S. Scannella, and F. Dehais, “Real-time state estimation in a flight simulator using fNIRS,” PLoS One 10(3), e0121279 (2015).
[Crossref] [PubMed]

Edelmann, J.

R. Zimmermann, L. Marchal-Crespo, J. Edelmann, O. Lambercy, M. C. Fluet, R. Riener, M. Wolf, and R. Gassert, “Detection of motor execution using a hybrid fNIRS-biosignal BCI: a feasibility study,” J. Neuroeng. Rehabil. 10(1), 4 (2013).
[Crossref] [PubMed]

Elwell, C. E.

D. A. Boas, C. E. Elwell, M. Ferrari, and G. Taga, “Twenty years of functional near-infrared spectroscopy: Introduction for the special issue,” Neuroimage 85(Pt 1), 1–5 (2014).
[Crossref] [PubMed]

Ernst, T.

T. Ernst and J. Hennig, “Observation of a fast response in functional MR,” Magn. Reson. Med. 32(1), 146–149 (1994).
[Crossref] [PubMed]

Fawcett, T.

T. Fawcett, “An introduction to ROC analysis,” Pattern Recognit. Lett. 27(8), 861–874 (2006).
[Crossref]

Ferrari, M.

D. A. Boas, C. E. Elwell, M. Ferrari, and G. Taga, “Twenty years of functional near-infrared spectroscopy: Introduction for the special issue,” Neuroimage 85(Pt 1), 1–5 (2014).
[Crossref] [PubMed]

M. Ferrari and V. Quaresima, “A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application,” Neuroimage 63(2), 921–935 (2012).
[Crossref] [PubMed]

Fluet, M. C.

R. Zimmermann, L. Marchal-Crespo, J. Edelmann, O. Lambercy, M. C. Fluet, R. Riener, M. Wolf, and R. Gassert, “Detection of motor execution using a hybrid fNIRS-biosignal BCI: a feasibility study,” J. Neuroeng. Rehabil. 10(1), 4 (2013).
[Crossref] [PubMed]

Fortmann, O.

C. Herff, D. Heger, O. Fortmann, J. Hennrich, F. Putze, and T. Schultz, “Mental workload during n-back task-quantified in the prefrontal cortex using fNIRS,” Front. Hum. Neurosci. 7, 935 (2014).
[Crossref] [PubMed]

Frank, L. R.

R. B. Buxton, E. C. Wong, and L. R. Frank, “Dynamics of blood flow and oxygenation changes during brain activation: the balloon model,” Magn. Reson. Med. 39(6), 855–864 (1998).
[Crossref] [PubMed]

Frostig, R. D.

A. Grinvald, R. D. Frostig, R. M. Siegel, and E. Bartfeld, “High-resolution optical imaging of functional brain architecture in the awake monkey,” Proc. Natl. Acad. Sci. U.S.A. 88(24), 11559–11563 (1991).
[Crossref] [PubMed]

R. D. Frostig, E. E. Lieke, D. Y. Ts’o, and A. Grinvald, “Cortical functional architecture and local coupling between neuronal activity and the microcirculation revealed by in vivo high-resolution optical imaging of intrinsic signals,” Proc. Natl. Acad. Sci. U.S.A. 87(16), 6082–6086 (1990).
[Crossref] [PubMed]

Gassert, R.

R. Zimmermann, L. Marchal-Crespo, J. Edelmann, O. Lambercy, M. C. Fluet, R. Riener, M. Wolf, and R. Gassert, “Detection of motor execution using a hybrid fNIRS-biosignal BCI: a feasibility study,” J. Neuroeng. Rehabil. 10(1), 4 (2013).
[Crossref] [PubMed]

Gateau, T.

T. Gateau, G. Durantin, F. Lancelot, S. Scannella, and F. Dehais, “Real-time state estimation in a flight simulator using fNIRS,” PLoS One 10(3), e0121279 (2015).
[Crossref] [PubMed]

Ge, S. S.

X.-S. Hu, K.-S. Hong, and S. S. Ge, “Reduction of trial-to-trial variability in functional near-infrared spectroscopy signals by accounting for resting-state functional connectivity,” J. Biomed. Opt. 18(1), 017003 (2013).
[Crossref] [PubMed]

Glover, G. H.

Gomez-Gil, J.

L. F. Nicolas-Alonso and J. Gomez-Gil, “Brain computer interfaces, a review,” Sensors (Basel) 12(12), 1211–1279 (2012).
[Crossref] [PubMed]

Graber, H. L.

G. R. Wylie, H. L. Graber, G. T. Voelbel, A. D. Kohl, J. DeLuca, Y. Pei, Y. Xu, and R. L. Barbour, “Using co-variations in the Hb signal to detect visual activation: a near infrared spectroscopic imaging study,” Neuroimage 47(2), 473–481 (2009).
[Crossref] [PubMed]

Grinvald, A.

D. Malonek and A. Grinvald, “Interactions between electrical activity and cortical microcirculation revealed by imaging spectroscopy: implications for functional brain mapping,” Science 272(5261), 551–554 (1996).
[Crossref] [PubMed]

A. Grinvald, R. D. Frostig, R. M. Siegel, and E. Bartfeld, “High-resolution optical imaging of functional brain architecture in the awake monkey,” Proc. Natl. Acad. Sci. U.S.A. 88(24), 11559–11563 (1991).
[Crossref] [PubMed]

R. D. Frostig, E. E. Lieke, D. Y. Ts’o, and A. Grinvald, “Cortical functional architecture and local coupling between neuronal activity and the microcirculation revealed by in vivo high-resolution optical imaging of intrinsic signals,” Proc. Natl. Acad. Sci. U.S.A. 87(16), 6082–6086 (1990).
[Crossref] [PubMed]

Guggenberger, H.

N. K. Logothetis, H. Guggenberger, S. Peled, and J. Pauls, “Functional imaging of the monkey brain,” Nat. Neurosci. 2(6), 555–562 (1999).
[Crossref] [PubMed]

Heger, D.

C. Herff, D. Heger, O. Fortmann, J. Hennrich, F. Putze, and T. Schultz, “Mental workload during n-back task-quantified in the prefrontal cortex using fNIRS,” Front. Hum. Neurosci. 7, 935 (2014).
[Crossref] [PubMed]

Hennig, J.

T. Ernst and J. Hennig, “Observation of a fast response in functional MR,” Magn. Reson. Med. 32(1), 146–149 (1994).
[Crossref] [PubMed]

Hennrich, J.

C. Herff, D. Heger, O. Fortmann, J. Hennrich, F. Putze, and T. Schultz, “Mental workload during n-back task-quantified in the prefrontal cortex using fNIRS,” Front. Hum. Neurosci. 7, 935 (2014).
[Crossref] [PubMed]

Herff, C.

C. Herff, D. Heger, O. Fortmann, J. Hennrich, F. Putze, and T. Schultz, “Mental workload during n-back task-quantified in the prefrontal cortex using fNIRS,” Front. Hum. Neurosci. 7, 935 (2014).
[Crossref] [PubMed]

Hong, K. S.

H.-D. Nguyen, K. S. Hong, and Y. I. Shin, “Bundled optode method in functional near-infrared spectroscopy,” PLoS One 11(10), e0165146 (2016).
[Crossref] [PubMed]

Hong, K.-S.

K.-S. Hong and N. Naseer, “Reduction of delay in detecting initial dips from functional near-infrared spectroscopy signals using vector-based phase analysis,” Int. J. Neural Syst. 26(3), 1650012 (2016).
[Crossref] [PubMed]

N. Naseer, F. M. Noori, N. K. Qureshi, and K.-S. Hong, “Determining optimal feature-combination for LDA classification of functional near-infrared spectroscopy signals in brain-computer interface application,” Front. Hum. Neurosci. 10, 237 (2016).
[Crossref] [PubMed]

K.-S. Hong and H. Santosa, “Decoding four different sound-categories in the auditory cortex using functional near-infrared spectroscopy,” Hear. Res. 333, 157–166 (2016).
[Crossref] [PubMed]

K.-S. Hong, N. Naseer, and Y.-H. Kim, “Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI,” Neurosci. Lett. 587, 87–92 (2015).
[Crossref] [PubMed]

M. R. Bhutta, M. J. Hong, Y.-H. Kim, and K.-S. Hong, “Single-trial lie detection using a combined fNIRS-polygraph system,” Front. Psychol. 6, 709 (2015).
[Crossref] [PubMed]

N. Naseer and K.-S. Hong, “fNIRS-based brain-computer interfaces: a review,” Front. Hum. Neurosci. 9, 3 (2015).
[PubMed]

N. Naseer and K.-S. Hong, “Decoding answers to four-choice questions using functional near infrared spectroscopy,” J. Near Infrared Spectrosc. 23(1), 23–31 (2015).
[Crossref]

M. J. Khan and K.-S. Hong, “Passive BCI based on drowsiness detection: an fNIRS study,” Biomed. Opt. Express 6(10), 4063–4078 (2015).
[Crossref] [PubMed]

H. Santosa, M. J. Hong, and K.-S. Hong, “Lateralization of music processing with noise in the auditory cortex: an fNIRS study,” Front. Behav. Neurosci. 8, 418 (2014).

K.-S. Hong and H.-D. Nguyen, “State-space models of impulse hemodynamic responses over motor, somatosensory, and visual cortices,” Biomed. Opt. Express 5(6), 1778–1798 (2014).
[Crossref] [PubMed]

M. R. Bhutta, K.-S. Hong, B.-M. Kim, M. J. Hong, Y.-H. Kim, and S.-H. Lee, “Note: three wavelengths near-infrared spectroscopy system for compensating the light absorbance by water,” Rev. Sci. Instrum. 85(2), 026111 (2014).
[Crossref] [PubMed]

N. Naseer, M. J. Hong, and K.-S. Hong, “Online binary decision decoding using functional near-infrared spectroscopy for the development of brain-computer interface,” Exp. Brain Res. 232(2), 555–564 (2014).
[Crossref] [PubMed]

M. J. Khan, M. J. Hong, and K.-S. Hong, “Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface,” Front. Hum. Neurosci. 8, 244 (2014).
[Crossref] [PubMed]

H. Santosa, M. J. Hong, S.-P. Kim, and K.-S. Hong, “Noise reduction in functional near-infrared spectroscopy signals by independent component analysis,” Rev. Sci. Instrum. 84(7), 073106 (2013).
[Crossref] [PubMed]

N. Naseer and K.-S. Hong, “Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain-computer interface,” Neurosci. Lett. 553, 84–89 (2013).
[Crossref] [PubMed]

X.-S. Hu, K.-S. Hong, and S. S. Ge, “Reduction of trial-to-trial variability in functional near-infrared spectroscopy signals by accounting for resting-state functional connectivity,” J. Biomed. Opt. 18(1), 017003 (2013).
[Crossref] [PubMed]

Hong, M. J.

M. R. Bhutta, M. J. Hong, Y.-H. Kim, and K.-S. Hong, “Single-trial lie detection using a combined fNIRS-polygraph system,” Front. Psychol. 6, 709 (2015).
[Crossref] [PubMed]

M. J. Khan, M. J. Hong, and K.-S. Hong, “Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface,” Front. Hum. Neurosci. 8, 244 (2014).
[Crossref] [PubMed]

N. Naseer, M. J. Hong, and K.-S. Hong, “Online binary decision decoding using functional near-infrared spectroscopy for the development of brain-computer interface,” Exp. Brain Res. 232(2), 555–564 (2014).
[Crossref] [PubMed]

M. R. Bhutta, K.-S. Hong, B.-M. Kim, M. J. Hong, Y.-H. Kim, and S.-H. Lee, “Note: three wavelengths near-infrared spectroscopy system for compensating the light absorbance by water,” Rev. Sci. Instrum. 85(2), 026111 (2014).
[Crossref] [PubMed]

H. Santosa, M. J. Hong, and K.-S. Hong, “Lateralization of music processing with noise in the auditory cortex: an fNIRS study,” Front. Behav. Neurosci. 8, 418 (2014).

H. Santosa, M. J. Hong, S.-P. Kim, and K.-S. Hong, “Noise reduction in functional near-infrared spectroscopy signals by independent component analysis,” Rev. Sci. Instrum. 84(7), 073106 (2013).
[Crossref] [PubMed]

Hu, X.

X. Hu and E. Yacoub, “The story of the initial dip in fMRI,” Neuroimage 62(2), 1103–1108 (2012).
[Crossref] [PubMed]

E. Yacoub and X. Hu, “Detection of the early decrease in fMRI signal in the motor area,” Magn. Reson. Med. 45(2), 184–190 (2001).
[Crossref] [PubMed]

E. Yacoub and X. Hu, “Detection of the early negative response in fMRI at 1.5 Tesla,” Magn. Reson. Med. 41(6), 1088–1092 (1999).
[Crossref] [PubMed]

X. Hu, T. H. Le, and K. Uğurbil, “Evaluation of the early response in fMRI in individual subjects using short stimulus duration,” Magn. Reson. Med. 37(6), 877–884 (1997).
[Crossref] [PubMed]

R. S. Menon, S. Ogawa, X. Hu, J. P. Strupp, P. Anderson, and K. Uğurbil, “BOLD based functional MRI at 4 Tesla includes a capillary bed contribution: echo-planar imaging correlates with previous optical imaging using intrinsic signals,” Magn. Reson. Med. 33(3), 453–459 (1995).
[Crossref] [PubMed]

Hu, X.-S.

X.-S. Hu, K.-S. Hong, and S. S. Ge, “Reduction of trial-to-trial variability in functional near-infrared spectroscopy signals by accounting for resting-state functional connectivity,” J. Biomed. Opt. 18(1), 017003 (2013).
[Crossref] [PubMed]

Ito, Y.

M. Naito, Y. Michioka, K. Ozawa, Y. Ito, M. Kiguchi, and T. Kanazawa, “A communication means for totally locked-in ALS patients based on changes in cerebral blood volume measured with near-infrared light,” IEICE Trans. Inf. Syst. E90-D(7), 1028–1037 (2007).
[Crossref]

Izzetoglu, M.

H. Ayaz, P. A. Shewokis, M. Izzetoğlu, M. P. Čakır, and B. Onaral, “Tangram solved? Prefrontal cortex activation analysis during geometric problem solving,” in Proceedings of IEEE Engineering in Medicine and Biology Society (IEEE, 2013), 4724–4727.

Jasdzewski, G.

G. Jasdzewski, G. Strangman, J. Wagner, K. K. Kwong, R. A. Poldrack, and D. A. Boas, “Differences in the hemodynamic response to event-related motor and visual paradigms as measured by near-infrared spectroscopy,” Neuroimage 20(1), 479–488 (2003).
[Crossref] [PubMed]

Johnston, D.

M. Jones, J. Berwick, D. Johnston, and J. Mayhew, “Concurrent optical imaging spectroscopy and laser-Doppler flowmetry: the relationship between blood flow, oxygenation, and volume in rodent barrel cortex,” Neuroimage 13(6), 1002–1015 (2001).
[Crossref] [PubMed]

Jones, M.

M. Jones, J. Berwick, D. Johnston, and J. Mayhew, “Concurrent optical imaging spectroscopy and laser-Doppler flowmetry: the relationship between blood flow, oxygenation, and volume in rodent barrel cortex,” Neuroimage 13(6), 1002–1015 (2001).
[Crossref] [PubMed]

Kanazawa, T.

M. Naito, Y. Michioka, K. Ozawa, Y. Ito, M. Kiguchi, and T. Kanazawa, “A communication means for totally locked-in ALS patients based on changes in cerebral blood volume measured with near-infrared light,” IEICE Trans. Inf. Syst. E90-D(7), 1028–1037 (2007).
[Crossref]

Kato, T.

N. Oka, K. Yoshino, K. Yamamoto, H. Takahashi, S. Li, T. Sugimachi, K. Nakano, Y. Suda, and T. Kato, “Greater activity in the frontal cortex on left curves: a vector-based fNIRS study of left and right curve driving,” PLoS One 10(5), e0127594 (2015).
[Crossref] [PubMed]

K. Yoshino, N. Oka, K. Yamamoto, H. Takahashi, and T. Kato, “Correlation of prefrontal cortical activation with changing vehicle speeds in actual driving: a vector-based functional near-infrared spectroscopy study,” Front. Hum. Neurosci. 7, 895 (2013).
[Crossref] [PubMed]

K. Yoshino and T. Kato, “Vector-based phase classification of initial dips during word listening using near-infrared spectroscopy,” Neuroreport 23(16), 947–951 (2012).
[Crossref] [PubMed]

T. Akiyama, T. Ohira, T. Kawase, and T. Kato, “TMS orientation for NIRS-functional motor mapping,” Brain Topogr. 19(1-2), 1–9 (2006).
[Crossref] [PubMed]

T. Kato, “Principle and technique of NIRS imaging for human brain FORCE: fast-oxygen response in capillary event,” Proc. ISBET1270, 85–90 (2004).
[Crossref]

Kawase, T.

T. Akiyama, T. Ohira, T. Kawase, and T. Kato, “TMS orientation for NIRS-functional motor mapping,” Brain Topogr. 19(1-2), 1–9 (2006).
[Crossref] [PubMed]

Khan, M. J.

M. J. Khan and K.-S. Hong, “Passive BCI based on drowsiness detection: an fNIRS study,” Biomed. Opt. Express 6(10), 4063–4078 (2015).
[Crossref] [PubMed]

M. J. Khan, M. J. Hong, and K.-S. Hong, “Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface,” Front. Hum. Neurosci. 8, 244 (2014).
[Crossref] [PubMed]

Kiguchi, M.

M. Naito, Y. Michioka, K. Ozawa, Y. Ito, M. Kiguchi, and T. Kanazawa, “A communication means for totally locked-in ALS patients based on changes in cerebral blood volume measured with near-infrared light,” IEICE Trans. Inf. Syst. E90-D(7), 1028–1037 (2007).
[Crossref]

Kim, B.-M.

M. R. Bhutta, K.-S. Hong, B.-M. Kim, M. J. Hong, Y.-H. Kim, and S.-H. Lee, “Note: three wavelengths near-infrared spectroscopy system for compensating the light absorbance by water,” Rev. Sci. Instrum. 85(2), 026111 (2014).
[Crossref] [PubMed]

Kim, D. S.

D. S. Kim, T. Q. Duong, and S. G. Kim, “High-resolution mapping of iso-orientation columns by fMRI,” Nat. Neurosci. 3(2), 164–169 (2000).
[Crossref] [PubMed]

T. Q. Duong, D. S. Kim, K. Uğurbil, and S. G. Kim, “Spatiotemporal dynamics of the BOLD fMRI signals: toward mapping submillimeter cortical columns using the early negative response,” Magn. Reson. Med. 44(2), 231–242 (2000).
[Crossref] [PubMed]

Kim, S. G.

T. Q. Duong, D. S. Kim, K. Uğurbil, and S. G. Kim, “Spatiotemporal dynamics of the BOLD fMRI signals: toward mapping submillimeter cortical columns using the early negative response,” Magn. Reson. Med. 44(2), 231–242 (2000).
[Crossref] [PubMed]

D. S. Kim, T. Q. Duong, and S. G. Kim, “High-resolution mapping of iso-orientation columns by fMRI,” Nat. Neurosci. 3(2), 164–169 (2000).
[Crossref] [PubMed]

Kim, S.-P.

H. Santosa, M. J. Hong, S.-P. Kim, and K.-S. Hong, “Noise reduction in functional near-infrared spectroscopy signals by independent component analysis,” Rev. Sci. Instrum. 84(7), 073106 (2013).
[Crossref] [PubMed]

Kim, Y.-H.

M. R. Bhutta, M. J. Hong, Y.-H. Kim, and K.-S. Hong, “Single-trial lie detection using a combined fNIRS-polygraph system,” Front. Psychol. 6, 709 (2015).
[Crossref] [PubMed]

K.-S. Hong, N. Naseer, and Y.-H. Kim, “Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI,” Neurosci. Lett. 587, 87–92 (2015).
[Crossref] [PubMed]

M. R. Bhutta, K.-S. Hong, B.-M. Kim, M. J. Hong, Y.-H. Kim, and S.-H. Lee, “Note: three wavelengths near-infrared spectroscopy system for compensating the light absorbance by water,” Rev. Sci. Instrum. 85(2), 026111 (2014).
[Crossref] [PubMed]

Kohl, A. D.

G. R. Wylie, H. L. Graber, G. T. Voelbel, A. D. Kohl, J. DeLuca, Y. Pei, Y. Xu, and R. L. Barbour, “Using co-variations in the Hb signal to detect visual activation: a near infrared spectroscopic imaging study,” Neuroimage 47(2), 473–481 (2009).
[Crossref] [PubMed]

Kushki, A.

S. D. Power, A. Kushki, and T. Chau, “Automatic single-trial discrimination of mental arithmetic, mental singing and the no-control state from prefrontal activity: toward a three-state NIRS-BCI,” BMC Res. Notes 5(1), 141 (2012).
[Crossref] [PubMed]

S. D. Power, A. Kushki, and T. Chau, “Towards a system-paced near-infrared spectroscopy brain-computer interface: differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state,” J. Neural Eng. 8(6), 066004 (2011).
[Crossref] [PubMed]

Kwong, K. K.

G. Jasdzewski, G. Strangman, J. Wagner, K. K. Kwong, R. A. Poldrack, and D. A. Boas, “Differences in the hemodynamic response to event-related motor and visual paradigms as measured by near-infrared spectroscopy,” Neuroimage 20(1), 479–488 (2003).
[Crossref] [PubMed]

Lambercy, O.

R. Zimmermann, L. Marchal-Crespo, J. Edelmann, O. Lambercy, M. C. Fluet, R. Riener, M. Wolf, and R. Gassert, “Detection of motor execution using a hybrid fNIRS-biosignal BCI: a feasibility study,” J. Neuroeng. Rehabil. 10(1), 4 (2013).
[Crossref] [PubMed]

Lancelot, F.

T. Gateau, G. Durantin, F. Lancelot, S. Scannella, and F. Dehais, “Real-time state estimation in a flight simulator using fNIRS,” PLoS One 10(3), e0121279 (2015).
[Crossref] [PubMed]

Le, T. H.

X. Hu, T. H. Le, and K. Uğurbil, “Evaluation of the early response in fMRI in individual subjects using short stimulus duration,” Magn. Reson. Med. 37(6), 877–884 (1997).
[Crossref] [PubMed]

Lee, S.-H.

M. R. Bhutta, K.-S. Hong, B.-M. Kim, M. J. Hong, Y.-H. Kim, and S.-H. Lee, “Note: three wavelengths near-infrared spectroscopy system for compensating the light absorbance by water,” Rev. Sci. Instrum. 85(2), 026111 (2014).
[Crossref] [PubMed]

Li, S.

N. Oka, K. Yoshino, K. Yamamoto, H. Takahashi, S. Li, T. Sugimachi, K. Nakano, Y. Suda, and T. Kato, “Greater activity in the frontal cortex on left curves: a vector-based fNIRS study of left and right curve driving,” PLoS One 10(5), e0127594 (2015).
[Crossref] [PubMed]

Lieke, E. E.

R. D. Frostig, E. E. Lieke, D. Y. Ts’o, and A. Grinvald, “Cortical functional architecture and local coupling between neuronal activity and the microcirculation revealed by in vivo high-resolution optical imaging of intrinsic signals,” Proc. Natl. Acad. Sci. U.S.A. 87(16), 6082–6086 (1990).
[Crossref] [PubMed]

Liu, N.

Liu, T. T.

Y. Behzadi and T. T. Liu, “Caffeine reduces the initial dip in the visual BOLD response at 3 T,” Neuroimage 32(1), 9–15 (2006).
[Crossref] [PubMed]

R. B. Buxton, K. Uludağ, D. J. Dubowitz, and T. T. Liu, “Modeling the hemodynamic response to brain activation,” Neuroimage 23(Suppl 1), S220–S233 (2004).
[Crossref] [PubMed]

Logothetis, N. K.

M. Watanabe, A. Bartels, J. H. Macke, Y. Murayama, and N. K. Logothetis, “Temporal jitter of the BOLD signal reveals a reliable initial dip and improved spatial resolution,” Curr. Biol. 23(21), 2146–2150 (2013).
[Crossref] [PubMed]

N. K. Logothetis, H. Guggenberger, S. Peled, and J. Pauls, “Functional imaging of the monkey brain,” Nat. Neurosci. 2(6), 555–562 (1999).
[Crossref] [PubMed]

Macke, J. H.

M. Watanabe, A. Bartels, J. H. Macke, Y. Murayama, and N. K. Logothetis, “Temporal jitter of the BOLD signal reveals a reliable initial dip and improved spatial resolution,” Curr. Biol. 23(21), 2146–2150 (2013).
[Crossref] [PubMed]

Malonek, D.

D. Malonek and A. Grinvald, “Interactions between electrical activity and cortical microcirculation revealed by imaging spectroscopy: implications for functional brain mapping,” Science 272(5261), 551–554 (1996).
[Crossref] [PubMed]

Mandeville, J. B.

J. B. Mandeville, J. J. Marota, C. Ayata, M. A. Moskowitz, R. M. Weisskoff, and B. R. Rosen, “MRI measurement of the temporal evolution of relative CMRO2 during rat forepaw stimulation,” Magn. Reson. Med. 42(5), 944–951 (1999).
[Crossref] [PubMed]

Marchal-Crespo, L.

R. Zimmermann, L. Marchal-Crespo, J. Edelmann, O. Lambercy, M. C. Fluet, R. Riener, M. Wolf, and R. Gassert, “Detection of motor execution using a hybrid fNIRS-biosignal BCI: a feasibility study,” J. Neuroeng. Rehabil. 10(1), 4 (2013).
[Crossref] [PubMed]

Markham, C.

F. Matthews, B. A. Pearlmutter, T. E. Ward, C. Soraghan, and C. Markham, “Hemodynamics for brain computer interfaces,” IEEE Signal Process. Mag. 25(1), 87–94 (2008).
[Crossref]

Marota, J. J.

J. B. Mandeville, J. J. Marota, C. Ayata, M. A. Moskowitz, R. M. Weisskoff, and B. R. Rosen, “MRI measurement of the temporal evolution of relative CMRO2 during rat forepaw stimulation,” Magn. Reson. Med. 42(5), 944–951 (1999).
[Crossref] [PubMed]

Matthews, F.

F. Matthews, B. A. Pearlmutter, T. E. Ward, C. Soraghan, and C. Markham, “Hemodynamics for brain computer interfaces,” IEEE Signal Process. Mag. 25(1), 87–94 (2008).
[Crossref]

Mayhew, J.

M. Jones, J. Berwick, D. Johnston, and J. Mayhew, “Concurrent optical imaging spectroscopy and laser-Doppler flowmetry: the relationship between blood flow, oxygenation, and volume in rodent barrel cortex,” Neuroimage 13(6), 1002–1015 (2001).
[Crossref] [PubMed]

McFarland, D. J.

D. J. McFarland and J. R. Wolpaw, “Brain-computer interfaces for the operation of robotic and prosthetic devices,” Adv. Comput. 79, 169–187 (2010).
[Crossref]

J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “Brain-computer interfaces for communication and control,” Clin. Neurophysiol. 113(6), 767–791 (2002).
[Crossref] [PubMed]

Menon, R. S.

R. S. Menon, S. Ogawa, X. Hu, J. P. Strupp, P. Anderson, and K. Uğurbil, “BOLD based functional MRI at 4 Tesla includes a capillary bed contribution: echo-planar imaging correlates with previous optical imaging using intrinsic signals,” Magn. Reson. Med. 33(3), 453–459 (1995).
[Crossref] [PubMed]

Michioka, Y.

M. Naito, Y. Michioka, K. Ozawa, Y. Ito, M. Kiguchi, and T. Kanazawa, “A communication means for totally locked-in ALS patients based on changes in cerebral blood volume measured with near-infrared light,” IEICE Trans. Inf. Syst. E90-D(7), 1028–1037 (2007).
[Crossref]

Moskowitz, M. A.

J. B. Mandeville, J. J. Marota, C. Ayata, M. A. Moskowitz, R. M. Weisskoff, and B. R. Rosen, “MRI measurement of the temporal evolution of relative CMRO2 during rat forepaw stimulation,” Magn. Reson. Med. 42(5), 944–951 (1999).
[Crossref] [PubMed]

Murayama, Y.

M. Watanabe, A. Bartels, J. H. Macke, Y. Murayama, and N. K. Logothetis, “Temporal jitter of the BOLD signal reveals a reliable initial dip and improved spatial resolution,” Curr. Biol. 23(21), 2146–2150 (2013).
[Crossref] [PubMed]

Naito, M.

M. Naito, Y. Michioka, K. Ozawa, Y. Ito, M. Kiguchi, and T. Kanazawa, “A communication means for totally locked-in ALS patients based on changes in cerebral blood volume measured with near-infrared light,” IEICE Trans. Inf. Syst. E90-D(7), 1028–1037 (2007).
[Crossref]

Nakano, K.

N. Oka, K. Yoshino, K. Yamamoto, H. Takahashi, S. Li, T. Sugimachi, K. Nakano, Y. Suda, and T. Kato, “Greater activity in the frontal cortex on left curves: a vector-based fNIRS study of left and right curve driving,” PLoS One 10(5), e0127594 (2015).
[Crossref] [PubMed]

Naseer, N.

K.-S. Hong and N. Naseer, “Reduction of delay in detecting initial dips from functional near-infrared spectroscopy signals using vector-based phase analysis,” Int. J. Neural Syst. 26(3), 1650012 (2016).
[Crossref] [PubMed]

N. Naseer, F. M. Noori, N. K. Qureshi, and K.-S. Hong, “Determining optimal feature-combination for LDA classification of functional near-infrared spectroscopy signals in brain-computer interface application,” Front. Hum. Neurosci. 10, 237 (2016).
[Crossref] [PubMed]

K.-S. Hong, N. Naseer, and Y.-H. Kim, “Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI,” Neurosci. Lett. 587, 87–92 (2015).
[Crossref] [PubMed]

N. Naseer and K.-S. Hong, “Decoding answers to four-choice questions using functional near infrared spectroscopy,” J. Near Infrared Spectrosc. 23(1), 23–31 (2015).
[Crossref]

N. Naseer and K.-S. Hong, “fNIRS-based brain-computer interfaces: a review,” Front. Hum. Neurosci. 9, 3 (2015).
[PubMed]

N. Naseer, M. J. Hong, and K.-S. Hong, “Online binary decision decoding using functional near-infrared spectroscopy for the development of brain-computer interface,” Exp. Brain Res. 232(2), 555–564 (2014).
[Crossref] [PubMed]

N. Naseer and K.-S. Hong, “Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain-computer interface,” Neurosci. Lett. 553, 84–89 (2013).
[Crossref] [PubMed]

Nguyen, H.-D.

H.-D. Nguyen, K. S. Hong, and Y. I. Shin, “Bundled optode method in functional near-infrared spectroscopy,” PLoS One 11(10), e0165146 (2016).
[Crossref] [PubMed]

K.-S. Hong and H.-D. Nguyen, “State-space models of impulse hemodynamic responses over motor, somatosensory, and visual cortices,” Biomed. Opt. Express 5(6), 1778–1798 (2014).
[Crossref] [PubMed]

Nicolas-Alonso, L. F.

L. F. Nicolas-Alonso and J. Gomez-Gil, “Brain computer interfaces, a review,” Sensors (Basel) 12(12), 1211–1279 (2012).
[Crossref] [PubMed]

Noori, F. M.

N. Naseer, F. M. Noori, N. K. Qureshi, and K.-S. Hong, “Determining optimal feature-combination for LDA classification of functional near-infrared spectroscopy signals in brain-computer interface application,” Front. Hum. Neurosci. 10, 237 (2016).
[Crossref] [PubMed]

Ogawa, S.

R. S. Menon, S. Ogawa, X. Hu, J. P. Strupp, P. Anderson, and K. Uğurbil, “BOLD based functional MRI at 4 Tesla includes a capillary bed contribution: echo-planar imaging correlates with previous optical imaging using intrinsic signals,” Magn. Reson. Med. 33(3), 453–459 (1995).
[Crossref] [PubMed]

Ohira, T.

T. Akiyama, T. Ohira, T. Kawase, and T. Kato, “TMS orientation for NIRS-functional motor mapping,” Brain Topogr. 19(1-2), 1–9 (2006).
[Crossref] [PubMed]

Oka, N.

N. Oka, K. Yoshino, K. Yamamoto, H. Takahashi, S. Li, T. Sugimachi, K. Nakano, Y. Suda, and T. Kato, “Greater activity in the frontal cortex on left curves: a vector-based fNIRS study of left and right curve driving,” PLoS One 10(5), e0127594 (2015).
[Crossref] [PubMed]

K. Yoshino, N. Oka, K. Yamamoto, H. Takahashi, and T. Kato, “Correlation of prefrontal cortical activation with changing vehicle speeds in actual driving: a vector-based functional near-infrared spectroscopy study,” Front. Hum. Neurosci. 7, 895 (2013).
[Crossref] [PubMed]

Onaral, B.

H. Ayaz, P. A. Shewokis, M. Izzetoğlu, M. P. Čakır, and B. Onaral, “Tangram solved? Prefrontal cortex activation analysis during geometric problem solving,” in Proceedings of IEEE Engineering in Medicine and Biology Society (IEEE, 2013), 4724–4727.

Ortiz-Rosario, A.

A. Ortiz-Rosario and H. Adeli, “Brain-computer interface technologies: from signal to action,” Rev. Neurosci. 24(5), 537–552 (2013).
[Crossref] [PubMed]

Ozawa, K.

M. Naito, Y. Michioka, K. Ozawa, Y. Ito, M. Kiguchi, and T. Kanazawa, “A communication means for totally locked-in ALS patients based on changes in cerebral blood volume measured with near-infrared light,” IEICE Trans. Inf. Syst. E90-D(7), 1028–1037 (2007).
[Crossref]

Pauls, J.

N. K. Logothetis, H. Guggenberger, S. Peled, and J. Pauls, “Functional imaging of the monkey brain,” Nat. Neurosci. 2(6), 555–562 (1999).
[Crossref] [PubMed]

Pearlmutter, B. A.

F. Matthews, B. A. Pearlmutter, T. E. Ward, C. Soraghan, and C. Markham, “Hemodynamics for brain computer interfaces,” IEEE Signal Process. Mag. 25(1), 87–94 (2008).
[Crossref]

Pei, Y.

G. R. Wylie, H. L. Graber, G. T. Voelbel, A. D. Kohl, J. DeLuca, Y. Pei, Y. Xu, and R. L. Barbour, “Using co-variations in the Hb signal to detect visual activation: a near infrared spectroscopic imaging study,” Neuroimage 47(2), 473–481 (2009).
[Crossref] [PubMed]

Peled, S.

N. K. Logothetis, H. Guggenberger, S. Peled, and J. Pauls, “Functional imaging of the monkey brain,” Nat. Neurosci. 2(6), 555–562 (1999).
[Crossref] [PubMed]

Pfurtscheller, G.

J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “Brain-computer interfaces for communication and control,” Clin. Neurophysiol. 113(6), 767–791 (2002).
[Crossref] [PubMed]

Poldrack, R. A.

G. Jasdzewski, G. Strangman, J. Wagner, K. K. Kwong, R. A. Poldrack, and D. A. Boas, “Differences in the hemodynamic response to event-related motor and visual paradigms as measured by near-infrared spectroscopy,” Neuroimage 20(1), 479–488 (2003).
[Crossref] [PubMed]

Power, S. D.

S. D. Power, A. Kushki, and T. Chau, “Automatic single-trial discrimination of mental arithmetic, mental singing and the no-control state from prefrontal activity: toward a three-state NIRS-BCI,” BMC Res. Notes 5(1), 141 (2012).
[Crossref] [PubMed]

S. D. Power, A. Kushki, and T. Chau, “Towards a system-paced near-infrared spectroscopy brain-computer interface: differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state,” J. Neural Eng. 8(6), 066004 (2011).
[Crossref] [PubMed]

Putze, F.

C. Herff, D. Heger, O. Fortmann, J. Hennrich, F. Putze, and T. Schultz, “Mental workload during n-back task-quantified in the prefrontal cortex using fNIRS,” Front. Hum. Neurosci. 7, 935 (2014).
[Crossref] [PubMed]

Quaresima, V.

M. Ferrari and V. Quaresima, “A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application,” Neuroimage 63(2), 921–935 (2012).
[Crossref] [PubMed]

Qureshi, N. K.

N. Naseer, F. M. Noori, N. K. Qureshi, and K.-S. Hong, “Determining optimal feature-combination for LDA classification of functional near-infrared spectroscopy signals in brain-computer interface application,” Front. Hum. Neurosci. 10, 237 (2016).
[Crossref] [PubMed]

Reiss, A. L.

Riener, R.

R. Zimmermann, L. Marchal-Crespo, J. Edelmann, O. Lambercy, M. C. Fluet, R. Riener, M. Wolf, and R. Gassert, “Detection of motor execution using a hybrid fNIRS-biosignal BCI: a feasibility study,” J. Neuroeng. Rehabil. 10(1), 4 (2013).
[Crossref] [PubMed]

Rosen, B. R.

J. B. Mandeville, J. J. Marota, C. Ayata, M. A. Moskowitz, R. M. Weisskoff, and B. R. Rosen, “MRI measurement of the temporal evolution of relative CMRO2 during rat forepaw stimulation,” Magn. Reson. Med. 42(5), 944–951 (1999).
[Crossref] [PubMed]

Santosa, H.

K.-S. Hong and H. Santosa, “Decoding four different sound-categories in the auditory cortex using functional near-infrared spectroscopy,” Hear. Res. 333, 157–166 (2016).
[Crossref] [PubMed]

H. Santosa, M. J. Hong, and K.-S. Hong, “Lateralization of music processing with noise in the auditory cortex: an fNIRS study,” Front. Behav. Neurosci. 8, 418 (2014).

H. Santosa, M. J. Hong, S.-P. Kim, and K.-S. Hong, “Noise reduction in functional near-infrared spectroscopy signals by independent component analysis,” Rev. Sci. Instrum. 84(7), 073106 (2013).
[Crossref] [PubMed]

Scannella, S.

T. Gateau, G. Durantin, F. Lancelot, S. Scannella, and F. Dehais, “Real-time state estimation in a flight simulator using fNIRS,” PLoS One 10(3), e0121279 (2015).
[Crossref] [PubMed]

Schudlo, L. C.

L. C. Schudlo and T. Chau, “Dynamic topographical pattern classification of multichannel prefrontal NIRS signals: II. Online differentiation of mental arithmetic and rest,” J. Neural Eng. 11(1), 016003 (2014).
[Crossref] [PubMed]

Schultz, T.

C. Herff, D. Heger, O. Fortmann, J. Hennrich, F. Putze, and T. Schultz, “Mental workload during n-back task-quantified in the prefrontal cortex using fNIRS,” Front. Hum. Neurosci. 7, 935 (2014).
[Crossref] [PubMed]

Shewokis, P. A.

H. Ayaz, P. A. Shewokis, M. Izzetoğlu, M. P. Čakır, and B. Onaral, “Tangram solved? Prefrontal cortex activation analysis during geometric problem solving,” in Proceedings of IEEE Engineering in Medicine and Biology Society (IEEE, 2013), 4724–4727.

Shin, Y. I.

H.-D. Nguyen, K. S. Hong, and Y. I. Shin, “Bundled optode method in functional near-infrared spectroscopy,” PLoS One 11(10), e0165146 (2016).
[Crossref] [PubMed]

Siegel, R. M.

A. Grinvald, R. D. Frostig, R. M. Siegel, and E. Bartfeld, “High-resolution optical imaging of functional brain architecture in the awake monkey,” Proc. Natl. Acad. Sci. U.S.A. 88(24), 11559–11563 (1991).
[Crossref] [PubMed]

Soraghan, C.

F. Matthews, B. A. Pearlmutter, T. E. Ward, C. Soraghan, and C. Markham, “Hemodynamics for brain computer interfaces,” IEEE Signal Process. Mag. 25(1), 87–94 (2008).
[Crossref]

Strangman, G.

G. Jasdzewski, G. Strangman, J. Wagner, K. K. Kwong, R. A. Poldrack, and D. A. Boas, “Differences in the hemodynamic response to event-related motor and visual paradigms as measured by near-infrared spectroscopy,” Neuroimage 20(1), 479–488 (2003).
[Crossref] [PubMed]

Strangman, G. E.

Q. Zhang, E. N. Brown, and G. E. Strangman, “Adaptive filtering to reduce global interference in evoked brain activity detection: a human subject case study,” J. Biomed. Opt. 12(6), 064009 (2007).
[Crossref] [PubMed]

Strupp, J. P.

R. S. Menon, S. Ogawa, X. Hu, J. P. Strupp, P. Anderson, and K. Uğurbil, “BOLD based functional MRI at 4 Tesla includes a capillary bed contribution: echo-planar imaging correlates with previous optical imaging using intrinsic signals,” Magn. Reson. Med. 33(3), 453–459 (1995).
[Crossref] [PubMed]

Suda, Y.

N. Oka, K. Yoshino, K. Yamamoto, H. Takahashi, S. Li, T. Sugimachi, K. Nakano, Y. Suda, and T. Kato, “Greater activity in the frontal cortex on left curves: a vector-based fNIRS study of left and right curve driving,” PLoS One 10(5), e0127594 (2015).
[Crossref] [PubMed]

Sugimachi, T.

N. Oka, K. Yoshino, K. Yamamoto, H. Takahashi, S. Li, T. Sugimachi, K. Nakano, Y. Suda, and T. Kato, “Greater activity in the frontal cortex on left curves: a vector-based fNIRS study of left and right curve driving,” PLoS One 10(5), e0127594 (2015).
[Crossref] [PubMed]

Taga, G.

D. A. Boas, C. E. Elwell, M. Ferrari, and G. Taga, “Twenty years of functional near-infrared spectroscopy: Introduction for the special issue,” Neuroimage 85(Pt 1), 1–5 (2014).
[Crossref] [PubMed]

Takahashi, H.

N. Oka, K. Yoshino, K. Yamamoto, H. Takahashi, S. Li, T. Sugimachi, K. Nakano, Y. Suda, and T. Kato, “Greater activity in the frontal cortex on left curves: a vector-based fNIRS study of left and right curve driving,” PLoS One 10(5), e0127594 (2015).
[Crossref] [PubMed]

K. Yoshino, N. Oka, K. Yamamoto, H. Takahashi, and T. Kato, “Correlation of prefrontal cortical activation with changing vehicle speeds in actual driving: a vector-based functional near-infrared spectroscopy study,” Front. Hum. Neurosci. 7, 895 (2013).
[Crossref] [PubMed]

Takehara-Nishiuchi, K.

S. Weyand, K. Takehara-Nishiuchi, and T. Chau, “Weaning off mental tasks to achieve voluntary self-regulatory control of a near-infrared spectroscopy brain-computer interface,” IEEE Trans. Neural Syst. Rehabil. Eng. 23(4), 548–561 (2015).
[Crossref] [PubMed]

Ts’o, D. Y.

R. D. Frostig, E. E. Lieke, D. Y. Ts’o, and A. Grinvald, “Cortical functional architecture and local coupling between neuronal activity and the microcirculation revealed by in vivo high-resolution optical imaging of intrinsic signals,” Proc. Natl. Acad. Sci. U.S.A. 87(16), 6082–6086 (1990).
[Crossref] [PubMed]

Ugurbil, K.

T. Q. Duong, D. S. Kim, K. Uğurbil, and S. G. Kim, “Spatiotemporal dynamics of the BOLD fMRI signals: toward mapping submillimeter cortical columns using the early negative response,” Magn. Reson. Med. 44(2), 231–242 (2000).
[Crossref] [PubMed]

X. Hu, T. H. Le, and K. Uğurbil, “Evaluation of the early response in fMRI in individual subjects using short stimulus duration,” Magn. Reson. Med. 37(6), 877–884 (1997).
[Crossref] [PubMed]

R. S. Menon, S. Ogawa, X. Hu, J. P. Strupp, P. Anderson, and K. Uğurbil, “BOLD based functional MRI at 4 Tesla includes a capillary bed contribution: echo-planar imaging correlates with previous optical imaging using intrinsic signals,” Magn. Reson. Med. 33(3), 453–459 (1995).
[Crossref] [PubMed]

Uludag, K.

R. B. Buxton, K. Uludağ, D. J. Dubowitz, and T. T. Liu, “Modeling the hemodynamic response to brain activation,” Neuroimage 23(Suppl 1), S220–S233 (2004).
[Crossref] [PubMed]

van der Zee, P.

D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of optical pathlength through tissue from direct time of flight measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988).
[Crossref] [PubMed]

Vaughan, T. M.

J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “Brain-computer interfaces for communication and control,” Clin. Neurophysiol. 113(6), 767–791 (2002).
[Crossref] [PubMed]

Voelbel, G. T.

G. R. Wylie, H. L. Graber, G. T. Voelbel, A. D. Kohl, J. DeLuca, Y. Pei, Y. Xu, and R. L. Barbour, “Using co-variations in the Hb signal to detect visual activation: a near infrared spectroscopic imaging study,” Neuroimage 47(2), 473–481 (2009).
[Crossref] [PubMed]

Wagner, J.

G. Jasdzewski, G. Strangman, J. Wagner, K. K. Kwong, R. A. Poldrack, and D. A. Boas, “Differences in the hemodynamic response to event-related motor and visual paradigms as measured by near-infrared spectroscopy,” Neuroimage 20(1), 479–488 (2003).
[Crossref] [PubMed]

Ward, T. E.

F. Matthews, B. A. Pearlmutter, T. E. Ward, C. Soraghan, and C. Markham, “Hemodynamics for brain computer interfaces,” IEEE Signal Process. Mag. 25(1), 87–94 (2008).
[Crossref]

Watanabe, M.

M. Watanabe, A. Bartels, J. H. Macke, Y. Murayama, and N. K. Logothetis, “Temporal jitter of the BOLD signal reveals a reliable initial dip and improved spatial resolution,” Curr. Biol. 23(21), 2146–2150 (2013).
[Crossref] [PubMed]

Weisskoff, R. M.

J. B. Mandeville, J. J. Marota, C. Ayata, M. A. Moskowitz, R. M. Weisskoff, and B. R. Rosen, “MRI measurement of the temporal evolution of relative CMRO2 during rat forepaw stimulation,” Magn. Reson. Med. 42(5), 944–951 (1999).
[Crossref] [PubMed]

Weyand, S.

S. Weyand, K. Takehara-Nishiuchi, and T. Chau, “Weaning off mental tasks to achieve voluntary self-regulatory control of a near-infrared spectroscopy brain-computer interface,” IEEE Trans. Neural Syst. Rehabil. Eng. 23(4), 548–561 (2015).
[Crossref] [PubMed]

Wolf, M.

R. Zimmermann, L. Marchal-Crespo, J. Edelmann, O. Lambercy, M. C. Fluet, R. Riener, M. Wolf, and R. Gassert, “Detection of motor execution using a hybrid fNIRS-biosignal BCI: a feasibility study,” J. Neuroeng. Rehabil. 10(1), 4 (2013).
[Crossref] [PubMed]

Wolpaw, J. R.

D. J. McFarland and J. R. Wolpaw, “Brain-computer interfaces for the operation of robotic and prosthetic devices,” Adv. Comput. 79, 169–187 (2010).
[Crossref]

J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “Brain-computer interfaces for communication and control,” Clin. Neurophysiol. 113(6), 767–791 (2002).
[Crossref] [PubMed]

Wong, E. C.

R. B. Buxton, E. C. Wong, and L. R. Frank, “Dynamics of blood flow and oxygenation changes during brain activation: the balloon model,” Magn. Reson. Med. 39(6), 855–864 (1998).
[Crossref] [PubMed]

Wray, S.

D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of optical pathlength through tissue from direct time of flight measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988).
[Crossref] [PubMed]

Wyatt, J.

D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of optical pathlength through tissue from direct time of flight measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988).
[Crossref] [PubMed]

Wylie, G. R.

G. R. Wylie, H. L. Graber, G. T. Voelbel, A. D. Kohl, J. DeLuca, Y. Pei, Y. Xu, and R. L. Barbour, “Using co-variations in the Hb signal to detect visual activation: a near infrared spectroscopic imaging study,” Neuroimage 47(2), 473–481 (2009).
[Crossref] [PubMed]

Xu, Y.

G. R. Wylie, H. L. Graber, G. T. Voelbel, A. D. Kohl, J. DeLuca, Y. Pei, Y. Xu, and R. L. Barbour, “Using co-variations in the Hb signal to detect visual activation: a near infrared spectroscopic imaging study,” Neuroimage 47(2), 473–481 (2009).
[Crossref] [PubMed]

Yacoub, E.

X. Hu and E. Yacoub, “The story of the initial dip in fMRI,” Neuroimage 62(2), 1103–1108 (2012).
[Crossref] [PubMed]

E. Yacoub and X. Hu, “Detection of the early decrease in fMRI signal in the motor area,” Magn. Reson. Med. 45(2), 184–190 (2001).
[Crossref] [PubMed]

E. Yacoub and X. Hu, “Detection of the early negative response in fMRI at 1.5 Tesla,” Magn. Reson. Med. 41(6), 1088–1092 (1999).
[Crossref] [PubMed]

Yamamoto, K.

N. Oka, K. Yoshino, K. Yamamoto, H. Takahashi, S. Li, T. Sugimachi, K. Nakano, Y. Suda, and T. Kato, “Greater activity in the frontal cortex on left curves: a vector-based fNIRS study of left and right curve driving,” PLoS One 10(5), e0127594 (2015).
[Crossref] [PubMed]

K. Yoshino, N. Oka, K. Yamamoto, H. Takahashi, and T. Kato, “Correlation of prefrontal cortical activation with changing vehicle speeds in actual driving: a vector-based functional near-infrared spectroscopy study,” Front. Hum. Neurosci. 7, 895 (2013).
[Crossref] [PubMed]

Yoshino, K.

N. Oka, K. Yoshino, K. Yamamoto, H. Takahashi, S. Li, T. Sugimachi, K. Nakano, Y. Suda, and T. Kato, “Greater activity in the frontal cortex on left curves: a vector-based fNIRS study of left and right curve driving,” PLoS One 10(5), e0127594 (2015).
[Crossref] [PubMed]

K. Yoshino, N. Oka, K. Yamamoto, H. Takahashi, and T. Kato, “Correlation of prefrontal cortical activation with changing vehicle speeds in actual driving: a vector-based functional near-infrared spectroscopy study,” Front. Hum. Neurosci. 7, 895 (2013).
[Crossref] [PubMed]

K. Yoshino and T. Kato, “Vector-based phase classification of initial dips during word listening using near-infrared spectroscopy,” Neuroreport 23(16), 947–951 (2012).
[Crossref] [PubMed]

Zhang, Q.

Q. Zhang, E. N. Brown, and G. E. Strangman, “Adaptive filtering to reduce global interference in evoked brain activity detection: a human subject case study,” J. Biomed. Opt. 12(6), 064009 (2007).
[Crossref] [PubMed]

Zimmermann, R.

R. Zimmermann, L. Marchal-Crespo, J. Edelmann, O. Lambercy, M. C. Fluet, R. Riener, M. Wolf, and R. Gassert, “Detection of motor execution using a hybrid fNIRS-biosignal BCI: a feasibility study,” J. Neuroeng. Rehabil. 10(1), 4 (2013).
[Crossref] [PubMed]

Adv. Comput. (1)

D. J. McFarland and J. R. Wolpaw, “Brain-computer interfaces for the operation of robotic and prosthetic devices,” Adv. Comput. 79, 169–187 (2010).
[Crossref]

Biomed. Opt. Express (3)

BMC Res. Notes (1)

S. D. Power, A. Kushki, and T. Chau, “Automatic single-trial discrimination of mental arithmetic, mental singing and the no-control state from prefrontal activity: toward a three-state NIRS-BCI,” BMC Res. Notes 5(1), 141 (2012).
[Crossref] [PubMed]

BMJ (1)

B. Christie, “Doctors revise declaration of Helsinki,” BMJ 321(7266), 913 (2000).
[Crossref] [PubMed]

Brain Topogr. (1)

T. Akiyama, T. Ohira, T. Kawase, and T. Kato, “TMS orientation for NIRS-functional motor mapping,” Brain Topogr. 19(1-2), 1–9 (2006).
[Crossref] [PubMed]

Clin. Neurophysiol. (1)

J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “Brain-computer interfaces for communication and control,” Clin. Neurophysiol. 113(6), 767–791 (2002).
[Crossref] [PubMed]

Curr. Biol. (1)

M. Watanabe, A. Bartels, J. H. Macke, Y. Murayama, and N. K. Logothetis, “Temporal jitter of the BOLD signal reveals a reliable initial dip and improved spatial resolution,” Curr. Biol. 23(21), 2146–2150 (2013).
[Crossref] [PubMed]

Exp. Brain Res. (1)

N. Naseer, M. J. Hong, and K.-S. Hong, “Online binary decision decoding using functional near-infrared spectroscopy for the development of brain-computer interface,” Exp. Brain Res. 232(2), 555–564 (2014).
[Crossref] [PubMed]

Front. Behav. Neurosci. (1)

H. Santosa, M. J. Hong, and K.-S. Hong, “Lateralization of music processing with noise in the auditory cortex: an fNIRS study,” Front. Behav. Neurosci. 8, 418 (2014).

Front. Hum. Neurosci. (5)

K. Yoshino, N. Oka, K. Yamamoto, H. Takahashi, and T. Kato, “Correlation of prefrontal cortical activation with changing vehicle speeds in actual driving: a vector-based functional near-infrared spectroscopy study,” Front. Hum. Neurosci. 7, 895 (2013).
[Crossref] [PubMed]

C. Herff, D. Heger, O. Fortmann, J. Hennrich, F. Putze, and T. Schultz, “Mental workload during n-back task-quantified in the prefrontal cortex using fNIRS,” Front. Hum. Neurosci. 7, 935 (2014).
[Crossref] [PubMed]

M. J. Khan, M. J. Hong, and K.-S. Hong, “Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface,” Front. Hum. Neurosci. 8, 244 (2014).
[Crossref] [PubMed]

N. Naseer, F. M. Noori, N. K. Qureshi, and K.-S. Hong, “Determining optimal feature-combination for LDA classification of functional near-infrared spectroscopy signals in brain-computer interface application,” Front. Hum. Neurosci. 10, 237 (2016).
[Crossref] [PubMed]

N. Naseer and K.-S. Hong, “fNIRS-based brain-computer interfaces: a review,” Front. Hum. Neurosci. 9, 3 (2015).
[PubMed]

Front. Psychol. (1)

M. R. Bhutta, M. J. Hong, Y.-H. Kim, and K.-S. Hong, “Single-trial lie detection using a combined fNIRS-polygraph system,” Front. Psychol. 6, 709 (2015).
[Crossref] [PubMed]

Hear. Res. (1)

K.-S. Hong and H. Santosa, “Decoding four different sound-categories in the auditory cortex using functional near-infrared spectroscopy,” Hear. Res. 333, 157–166 (2016).
[Crossref] [PubMed]

IEEE Signal Process. Mag. (1)

F. Matthews, B. A. Pearlmutter, T. E. Ward, C. Soraghan, and C. Markham, “Hemodynamics for brain computer interfaces,” IEEE Signal Process. Mag. 25(1), 87–94 (2008).
[Crossref]

IEEE Trans. Neural Syst. Rehabil. Eng. (1)

S. Weyand, K. Takehara-Nishiuchi, and T. Chau, “Weaning off mental tasks to achieve voluntary self-regulatory control of a near-infrared spectroscopy brain-computer interface,” IEEE Trans. Neural Syst. Rehabil. Eng. 23(4), 548–561 (2015).
[Crossref] [PubMed]

IEICE Trans. Inf. Syst. (1)

M. Naito, Y. Michioka, K. Ozawa, Y. Ito, M. Kiguchi, and T. Kanazawa, “A communication means for totally locked-in ALS patients based on changes in cerebral blood volume measured with near-infrared light,” IEICE Trans. Inf. Syst. E90-D(7), 1028–1037 (2007).
[Crossref]

Int. J. Neural Syst. (1)

K.-S. Hong and N. Naseer, “Reduction of delay in detecting initial dips from functional near-infrared spectroscopy signals using vector-based phase analysis,” Int. J. Neural Syst. 26(3), 1650012 (2016).
[Crossref] [PubMed]

J. Biomed. Opt. (2)

X.-S. Hu, K.-S. Hong, and S. S. Ge, “Reduction of trial-to-trial variability in functional near-infrared spectroscopy signals by accounting for resting-state functional connectivity,” J. Biomed. Opt. 18(1), 017003 (2013).
[Crossref] [PubMed]

Q. Zhang, E. N. Brown, and G. E. Strangman, “Adaptive filtering to reduce global interference in evoked brain activity detection: a human subject case study,” J. Biomed. Opt. 12(6), 064009 (2007).
[Crossref] [PubMed]

J. Near Infrared Spectrosc. (1)

N. Naseer and K.-S. Hong, “Decoding answers to four-choice questions using functional near infrared spectroscopy,” J. Near Infrared Spectrosc. 23(1), 23–31 (2015).
[Crossref]

J. Neural Eng. (3)

L. C. Schudlo and T. Chau, “Dynamic topographical pattern classification of multichannel prefrontal NIRS signals: II. Online differentiation of mental arithmetic and rest,” J. Neural Eng. 11(1), 016003 (2014).
[Crossref] [PubMed]

L. C Schudlo and T. Chau, “Towards a ternary NIRS-BCI: single-trial classification of verbal fluency task, Stroop task and unconstrained rest,” J. Neural Eng. 12(6), 066008 (2015).
[Crossref] [PubMed]

S. D. Power, A. Kushki, and T. Chau, “Towards a system-paced near-infrared spectroscopy brain-computer interface: differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state,” J. Neural Eng. 8(6), 066004 (2011).
[Crossref] [PubMed]

J. Neuroeng. Rehabil. (1)

R. Zimmermann, L. Marchal-Crespo, J. Edelmann, O. Lambercy, M. C. Fluet, R. Riener, M. Wolf, and R. Gassert, “Detection of motor execution using a hybrid fNIRS-biosignal BCI: a feasibility study,” J. Neuroeng. Rehabil. 10(1), 4 (2013).
[Crossref] [PubMed]

Magn. Reson. Med. (8)

E. Yacoub and X. Hu, “Detection of the early decrease in fMRI signal in the motor area,” Magn. Reson. Med. 45(2), 184–190 (2001).
[Crossref] [PubMed]

X. Hu, T. H. Le, and K. Uğurbil, “Evaluation of the early response in fMRI in individual subjects using short stimulus duration,” Magn. Reson. Med. 37(6), 877–884 (1997).
[Crossref] [PubMed]

T. Ernst and J. Hennig, “Observation of a fast response in functional MR,” Magn. Reson. Med. 32(1), 146–149 (1994).
[Crossref] [PubMed]

J. B. Mandeville, J. J. Marota, C. Ayata, M. A. Moskowitz, R. M. Weisskoff, and B. R. Rosen, “MRI measurement of the temporal evolution of relative CMRO2 during rat forepaw stimulation,” Magn. Reson. Med. 42(5), 944–951 (1999).
[Crossref] [PubMed]

R. S. Menon, S. Ogawa, X. Hu, J. P. Strupp, P. Anderson, and K. Uğurbil, “BOLD based functional MRI at 4 Tesla includes a capillary bed contribution: echo-planar imaging correlates with previous optical imaging using intrinsic signals,” Magn. Reson. Med. 33(3), 453–459 (1995).
[Crossref] [PubMed]

E. Yacoub and X. Hu, “Detection of the early negative response in fMRI at 1.5 Tesla,” Magn. Reson. Med. 41(6), 1088–1092 (1999).
[Crossref] [PubMed]

T. Q. Duong, D. S. Kim, K. Uğurbil, and S. G. Kim, “Spatiotemporal dynamics of the BOLD fMRI signals: toward mapping submillimeter cortical columns using the early negative response,” Magn. Reson. Med. 44(2), 231–242 (2000).
[Crossref] [PubMed]

R. B. Buxton, E. C. Wong, and L. R. Frank, “Dynamics of blood flow and oxygenation changes during brain activation: the balloon model,” Magn. Reson. Med. 39(6), 855–864 (1998).
[Crossref] [PubMed]

Nat. Neurosci. (2)

N. K. Logothetis, H. Guggenberger, S. Peled, and J. Pauls, “Functional imaging of the monkey brain,” Nat. Neurosci. 2(6), 555–562 (1999).
[Crossref] [PubMed]

D. S. Kim, T. Q. Duong, and S. G. Kim, “High-resolution mapping of iso-orientation columns by fMRI,” Nat. Neurosci. 3(2), 164–169 (2000).
[Crossref] [PubMed]

Neuroimage (9)

M. Ferrari and V. Quaresima, “A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application,” Neuroimage 63(2), 921–935 (2012).
[Crossref] [PubMed]

M. Jones, J. Berwick, D. Johnston, and J. Mayhew, “Concurrent optical imaging spectroscopy and laser-Doppler flowmetry: the relationship between blood flow, oxygenation, and volume in rodent barrel cortex,” Neuroimage 13(6), 1002–1015 (2001).
[Crossref] [PubMed]

G. Jasdzewski, G. Strangman, J. Wagner, K. K. Kwong, R. A. Poldrack, and D. A. Boas, “Differences in the hemodynamic response to event-related motor and visual paradigms as measured by near-infrared spectroscopy,” Neuroimage 20(1), 479–488 (2003).
[Crossref] [PubMed]

R. B. Buxton, K. Uludağ, D. J. Dubowitz, and T. T. Liu, “Modeling the hemodynamic response to brain activation,” Neuroimage 23(Suppl 1), S220–S233 (2004).
[Crossref] [PubMed]

X. Hu and E. Yacoub, “The story of the initial dip in fMRI,” Neuroimage 62(2), 1103–1108 (2012).
[Crossref] [PubMed]

D. A. Boas, C. E. Elwell, M. Ferrari, and G. Taga, “Twenty years of functional near-infrared spectroscopy: Introduction for the special issue,” Neuroimage 85(Pt 1), 1–5 (2014).
[Crossref] [PubMed]

G. R. Wylie, H. L. Graber, G. T. Voelbel, A. D. Kohl, J. DeLuca, Y. Pei, Y. Xu, and R. L. Barbour, “Using co-variations in the Hb signal to detect visual activation: a near infrared spectroscopic imaging study,” Neuroimage 47(2), 473–481 (2009).
[Crossref] [PubMed]

Y. Behzadi and T. T. Liu, “Caffeine reduces the initial dip in the visual BOLD response at 3 T,” Neuroimage 32(1), 9–15 (2006).
[Crossref] [PubMed]

R. B. Buxton, “The elusive initial dip,” Neuroimage 13(6), 953–958 (2001).
[Crossref] [PubMed]

Neuroreport (1)

K. Yoshino and T. Kato, “Vector-based phase classification of initial dips during word listening using near-infrared spectroscopy,” Neuroreport 23(16), 947–951 (2012).
[Crossref] [PubMed]

Neurosci. Lett. (2)

N. Naseer and K.-S. Hong, “Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain-computer interface,” Neurosci. Lett. 553, 84–89 (2013).
[Crossref] [PubMed]

K.-S. Hong, N. Naseer, and Y.-H. Kim, “Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI,” Neurosci. Lett. 587, 87–92 (2015).
[Crossref] [PubMed]

Pattern Recognit. Lett. (1)

T. Fawcett, “An introduction to ROC analysis,” Pattern Recognit. Lett. 27(8), 861–874 (2006).
[Crossref]

Phys. Med. Biol. (1)

D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of optical pathlength through tissue from direct time of flight measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988).
[Crossref] [PubMed]

PLoS One (3)

H.-D. Nguyen, K. S. Hong, and Y. I. Shin, “Bundled optode method in functional near-infrared spectroscopy,” PLoS One 11(10), e0165146 (2016).
[Crossref] [PubMed]

N. Oka, K. Yoshino, K. Yamamoto, H. Takahashi, S. Li, T. Sugimachi, K. Nakano, Y. Suda, and T. Kato, “Greater activity in the frontal cortex on left curves: a vector-based fNIRS study of left and right curve driving,” PLoS One 10(5), e0127594 (2015).
[Crossref] [PubMed]

T. Gateau, G. Durantin, F. Lancelot, S. Scannella, and F. Dehais, “Real-time state estimation in a flight simulator using fNIRS,” PLoS One 10(3), e0121279 (2015).
[Crossref] [PubMed]

Proc. Natl. Acad. Sci. U.S.A. (2)

R. D. Frostig, E. E. Lieke, D. Y. Ts’o, and A. Grinvald, “Cortical functional architecture and local coupling between neuronal activity and the microcirculation revealed by in vivo high-resolution optical imaging of intrinsic signals,” Proc. Natl. Acad. Sci. U.S.A. 87(16), 6082–6086 (1990).
[Crossref] [PubMed]

A. Grinvald, R. D. Frostig, R. M. Siegel, and E. Bartfeld, “High-resolution optical imaging of functional brain architecture in the awake monkey,” Proc. Natl. Acad. Sci. U.S.A. 88(24), 11559–11563 (1991).
[Crossref] [PubMed]

Rev. Neurosci. (1)

A. Ortiz-Rosario and H. Adeli, “Brain-computer interface technologies: from signal to action,” Rev. Neurosci. 24(5), 537–552 (2013).
[Crossref] [PubMed]

Rev. Sci. Instrum. (2)

M. R. Bhutta, K.-S. Hong, B.-M. Kim, M. J. Hong, Y.-H. Kim, and S.-H. Lee, “Note: three wavelengths near-infrared spectroscopy system for compensating the light absorbance by water,” Rev. Sci. Instrum. 85(2), 026111 (2014).
[Crossref] [PubMed]

H. Santosa, M. J. Hong, S.-P. Kim, and K.-S. Hong, “Noise reduction in functional near-infrared spectroscopy signals by independent component analysis,” Rev. Sci. Instrum. 84(7), 073106 (2013).
[Crossref] [PubMed]

Science (1)

D. Malonek and A. Grinvald, “Interactions between electrical activity and cortical microcirculation revealed by imaging spectroscopy: implications for functional brain mapping,” Science 272(5261), 551–554 (1996).
[Crossref] [PubMed]

Sensors (Basel) (1)

L. F. Nicolas-Alonso and J. Gomez-Gil, “Brain computer interfaces, a review,” Sensors (Basel) 12(12), 1211–1279 (2012).
[Crossref] [PubMed]

Other (2)

T. Kato, “Principle and technique of NIRS imaging for human brain FORCE: fast-oxygen response in capillary event,” Proc. ISBET1270, 85–90 (2004).
[Crossref]

H. Ayaz, P. A. Shewokis, M. Izzetoğlu, M. P. Čakır, and B. Onaral, “Tangram solved? Prefrontal cortex activation analysis during geometric problem solving,” in Proceedings of IEEE Engineering in Medicine and Biology Society (IEEE, 2013), 4724–4727.

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Figures (10)

Fig. 1
Fig. 1 BCI framework: Application of initial dip detection.
Fig. 2
Fig. 2 Characteristics of the canonical initial dip.
Fig. 3
Fig. 3 Experimental paradigms: Experiment 1 consists of mental arithmetic (MA), mental counting (MC), and puzzle solving (PS) tasks; Experiment 2 is composed of right-hand finger tapping (RHFT) and right-hand finger poking (RHFP); Experiment 3 examines checkerboard visualization; and a session consists of 5 trials.
Fig. 4
Fig. 4 Electrode configuration in the prefrontal (Exp. 1), motor/somatosensory (Exp. 2), and visual cortices (Exp. 3).
Fig. 5
Fig. 5 Vector plane with a threshold circle [27–30].
Fig. 6
Fig. 6 Phase portraits (from −5 to 15 sec) of mental arithmetic task (Sub. 1).
Fig. 7
Fig. 7 The averaged HbOs and their standard deviations for MA, MC, and PS tasks.
Fig. 8
Fig. 8 Receiver operating characteristic (ROC) curves for HR-based classification of 2~7 sec window using the averaged TPR and FPR for the MA, MC, and PS tasks over all subjects.
Fig. 9
Fig. 9 ROC curves for the initial dip based classification in 0~2.5 sec window using the averaged TPR and FPR for the MA, MC, and PS tasks over all subjects.
Fig. 10
Fig. 10 The averaged HbO and its standard deviation for MA, MC, PS, RHFT, RHFP, and checkerboard tasks.

Tables (6)

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Table 1 List of channels in which initial dip has been detected.

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Table 2 Classification accuracies of three tasks based on hemodynamic responses.

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Table 3 Classification accuracies of three tasks for various feature combinations.

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Table 4 Classification accuracies of three tasks for various window sizes (used features: mean, delta).

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Table 5 Comparison between initial-dip-window and HR-window.

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Table 6 Initial dip characteristic parameters of MA, MC, PS, RHFT, RHFP, and checkerboard tasks.

Equations (8)

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ΔHbT= 1 2 (ΔHbO+ΔHbR),
ΔCOE= 1 2 (ΔHbRΔHbO),
| p |= ΔHb O 2 +ΔHb R 2 ,
p= tan 1 ( ΔCOE ΔHbT )+45°= tan 1 ( ΔHbR ΔHbO ).
y = ymin(y) max(y)min(y) ,
TPR= TP TP+FN ,
TNR= TN TN+FP ,
FPR= FP FP+TN =1TNR,

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