• Journal of Innovative Optical Health Sciences
  • Vol. 10, Issue 3, 1750006 (2017)
Xiaolong Liu1 and Keum-Shik Hong2、3、*
Author Affiliations
  • 1School of Mechanical Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 46241, Republic of Korea
  • 2School of Mechanical Engineering and Department of Cogno-Mechatronics Engineering, Pusan National University
  • 32 Busandaehak-ro, Geumjeong-gu, Busan 46241, Republic of Korea
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    DOI: 10.1142/s1793545817500067 Cite this Article
    Xiaolong Liu, Keum-Shik Hong. Detection of primary RGB colors projected on a screen using fNIRS[J]. Journal of Innovative Optical Health Sciences, 2017, 10(3): 1750006 Copy Citation Text show less
    References

    [1] Y. P. Xiao, Y. Wang, D. J. Felleman, “A spatially organized representation of colour in macaque Cortical Area V2, ” Nature 421, 535–539 (2003).

    [2] H. D. Lu, A. W. Roe, “Functional organization of color domains in V1 and V2 of macaque monkey revealed by optical imaging, ” Cereb. Cortex 18, 516–533 (2008).

    [3] H. Tanigawa, H. D. Lu, A. W. Roe, “Functional organization for color and orientation in macaque V4, ” Nat. Neurosci. 13, 1542–U135 (2010).

    [4] E. Goddard, D. J. Mannion, J. S. McDonald, S. G. Solomon, C. W. Clifford, “Combination of subcortical color channels in human visual cortex, ” J. Vision 10, 25 (2010).

    [5] G. J. Brouwer, D. J. Heeger, “Decoding and reconstructing color from responses in human visual cortex, ” J. Neurosci. 29, 13992–4003 (2009).

    [6] L. M. Parkes, J. B. C. Marsman, D. C. Oxley, J. Y. Goulermas, S. M. Wuerger, “Multivoxel fMRI analysis of color tuning in human primary visual cortex, ” J. Vision 9, 1 (2009).

    [7] K. T. Mullen, S. O. Dumoulin, K. L. McMahon, G. I. de Zubicaray, R. F. Hess, “Selectivity of human retinotopic visual cortex to s-cone-opponent, L/M-cone-opponent and achromatic stimulation, ” Eur. J. Neurosci. 25, 491–502 (2007).

    [8] K. T. Mullen, D. H. Chang, R. F. Hess, “The selectivity of responses to red-green colour and achromatic contrast in the human visual cortex: An fMRI adaptation study, ” Eur. J. Neurosci. 42, 2923–2933 (2015).

    [9] B. Laeng, K. Hugdahl, K. Specht, “The neural correlate of colour distances revealed with competing synaesthetic and real colours, ” Cortex 47, 320–331 (2011).

    [10] I. Kuriki, P. Sun, K. Ueno, K. Tanaka, K. Cheng, “Hue selectivity in human visual cortex revealed by functional magnetic resonance imaging, ” Cerb. Cortex 25, 4869–4884 (2015).

    [11] Z. Tang, , H. Zhang, “To judge what color the subject watched by color effect on brain activity, ” Int. J. Comput. Sci. Netw. Secur. 11, 80–83 (2011).

    [12] S. Rasheed, D. Marini, “Classification of EEG signals produced by RGB colour stimuli, ” J. Biomed. Eng. Med. Imag. 2, 56 (2015).

    [13] E. Alharbi, S. Rasheed, S. Buhari, “Single trial classification of evoked EEG signals due to RGB colors, ” Brain-Broad Res. Artif. Intell. Neurosci. 7, 29–41 (2016). ISI,

    [14] D. A. Boas, A. M. Dale, M. A. Franceschini, “Diffuse optical imaging of brain activation: Approaches to optimizing image sensitivity, resolution, and accuracy, ” Neuroimage 23, S275–S288 (2004).

    [15] M. Wolf, M. Ferrari, V. Quaresima, “Progress of near-infrared spectroscopy and topography for brain and muscle clinical applications, ” J. Biomed. Opt. 12, 062–104 (2007). ISI,

    [16] X.-S. Hu, K.-S. Hong, S. S. Ge, “Recognition of stimulus-evoked neuronal optical response by identifying chaos levels of near-infrared spectroscopy time series, ” Neurosci. Lett. 504, 115–120 (2011).

    [17] A. C. Merzagora, M. T. Schultheis, B. Onaral, M. Izzetoglu, “Functional near-infrared spectroscopy-based assessment of attention impairments after traumatic brain injury, ” J. Innov. Opt. Health Sci. 4, 251–260 (2011). Link, ISI,

    [18] M. J. Khan, X. Liu, M. R. Bhutta, K.-S. Hong, Drowsiness detection using fNIRS in different time windows for a passive BCI, In Biomedical Robotics and Biomechatronics (BioRob), 2016 6th IEEE Int. Conf. IEEE (2016), pp. 227–231.

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

    [20] S. Koehler, J. Egetemeir, P. Stenneken, S. P. Koch, P. Pauli, A. J. Fallgatter, M. J. Herrmann, “The human execution/observation matching system investigated with a complex everyday task: A functional near-infrared spectroscopy (fNIRS) Study, ” Neurosci. Lett. 508, 73–77 (2012).

    [21] H. Meiri, I. Sela, P. Nesher, M. Izzetoglu, K. Izzetoglu, B. Onaral, Z. Breznitz, “Frontal lobe role in simple arithmetic calculations: An fNIRS study, ” Neurosci. Lett. 510, 43–7 (2012).

    [22] N. Naseer, 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).

    [23] L. Zhang, J. Y. Sun, B. L. Sun, C. Y. Gao, H. Gong, “Detecting bilateral functional connectivity in the prefrontal cortex during a stroop task by near-infrared spectroscopy, ” J. Innov. Opt. Health. Sci. 6, 1350031 (2013). Link, ISI,

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

    [25] J. M. Kainerstorfer, A. Sassaroli, M. L. Pierro, B. Hallacoglu, S. Fantini, “Coherent hemodynamics spectroscopy based on a paced breathing paradigm-revisited, ” J. Innov. Opt. Health. Sci. 7, 145013 (2014). Link, ISI,

    [26] N. D. Thang, V. V. Toi, L. G. Tran, N. H. M. Tam, L. A. Trinh, “Investigation of human visual cortex responses to flickering light using functional near infrared spectroscopy and constrained ICA, ” J. Innov. Opt. Health Sci. 7, 1450031 (2014). Link, ISI,

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

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

    [29] T. Li, Y. Li, Y. L. Sun, M. X. Duan, L. Y. Peng, “Effect of head model on monte carlo modeling of spatial sensitivity distribution for functional near-infrared spectroscopy, ” J. Innov. Opt. Health Sci. 8, 1550024 (2015). Link, ISI,

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

    [31] D. P. Pinero, B. Monllor, V. Moncho, V. J. Camps, D. de Fez, “Visual function alterations in essential tremor: A case report, ” J. Innov. Opt. Health. Sci. 8, 1550040 (2015). Link, ISI,

    [32] A. Sassaroli, J. Kainerstorfer, S. Fantini, “Study of capillary transit time distribution in coherent hemodynamics spectroscopy, ” J. Innov. Opt. Health Sci. 8, 1550025 (2015). Link, ISI,

    [33] M. Shokoufi, F. Golnaraghi, “Development of a handheld diffuse optical breast cancer assessment probe, ” J. Innov. Opt. Health Sci. 9, 1650007 (2016). Link, ISI,

    [34] V. Y. Toronov, X. F. Zhang, A. G. Webb, “A spatial and temporal comparison of hemodynamic signals measured using optical and functional magnetic resonance imaging during activation in the human primary visual cortex, ” Neuroimage 34, 1136–1148 (2007).

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

    [36] X. S. Hu, K.-S. Hong, S. S. Ge, “fNIRS-based online deception decoding, ” J. Neural Eng. 9, 026012 (2012).

    [37] R. L. Barbour, H. L. Graber, Y. Pei, S. Zhong, C. H. Schmitz, “Optical tomographic imaging of dynamic features of dense-scattering media, ” J. Opt. Soc. Am. A-Opt. Image. Sci. Vis. 18, 3018–3036 (2001).

    [38] M. Ferrari, L. Mottola, V. Quaresima, “Principles, techniques, and limitations of near infrared spectroscopy, ” Can. J. Appl. Physiol. 29, 463–487 (2004).

    [39] A. Villringer, J. Planck, C. Hock, L. Schleinkofer, U. Dirnagl, “Near infrared spectroscopy (NIRS): A new tool to study hemodynamic changes during activation of brain function in human adults, ” Neurosci. Lett. 154, 101–104 (1993).

    [40] A. J. Fallgatter, M. Roesler, L. Sitzmann, A. Heidrich, T. J. Mueller, W. K. Strik, “Loss of functional hemispheric asymmetry in alzheimer”s dementia assessed with near-infrared spectroscopy, ” Cognit. Brain Res. 6, 67–72 (1997).

    [41] R. O. Duda, P. E. Hart, D. G. Stork, Pattern Classification, John Wiley & Sons (2012).

    [42] H. Liu, L. Yu, “Toward integrating feature selection algorithms for classification and clustering, ” IEEE Trans. Knowl. Data. Eng. 17, 491–502 (2005).

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

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

    [45] F. Putze, S. Hesslinger, C. Y. Tse, Y. Y. Huang, C. Herff, C. T. Guan, T. Schultz, “Hybrid fNIRS-EEG based classification of auditory and visual perception processes, ” Front. Neurosci. 8, 373 (2014).

    [46] F. Lotte, M. Congedo, A. Lecuyer, F. Lamarche, B. Arnaldi, “A review of classification algorithms for EEG-based brain-computer interfaces, ” J. Neural Eng. 4, R1–R13 (2007).

    [47] F. Pereira, T. Mitchell, M. Botvinick, “Machine learning classifiers and fMRI: A tutorial overview, ” Neuroimage 45, S199–S209 (2009).

    [48] D. Garrett, D. A. Peterson, C. W. Anderson, M. H. Thaut, “Comparison of linear, nonlinear, and feature selection methods for EEG signal classification, ” IEEE Trans. Neural Syst. Rehabil. Eng. 11, 141–144 (2003).

    [49] A. Schlogl, F. Lee, H. Bischof, G. Pfurtscheller, “Characterization of four-class motor imagery EEG data for the BCI-competition, ” J. Neural Eng. 2, L14–L22 (2005).

    [50] J. Chul, S. Tak, K. E. Jang, J. Jung, J. Jang, “NIRS-SPM: Statistical parametric mapping for near-infrared spectroscopy, ” Neuroimage 44, 428–447 (2009).

    [51] M. A. Kamran, K.-S. Hong, “Linear parameter-varying model and adaptive filtering technique for detecting neuronal activities: An fNIRS study, ” J. Neural. Eng. 10, 056002 (2013).

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

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

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

    [55] J. Tanabe, D. Miller, J. Tregellas, R. Freedman, F. G. Meyer, “Comparison of detrending methods for optimal fMRI preprocessing, ” Neuroimage 15, 902–907 (2002).

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

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

    [58] X. Liu, K.-S. Hong, fNIRS based color detection from human visual cortex, in Society of Instrument and Control Engineers of Japan (SICE), 2015 54th Annual Conf. IEEE (2015), pp. 1156–1161.

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

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

    [61] K.-S. Hong, 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, 1650012 (2016). Link, ISI,

    [62] N. Naseer, F. M. Noori, N. K. Qureshi, 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).

    [63] X.-S. Hu, K.-S. Hong, 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, 017003 (2013).

    [64] H.-D. Nguyen, K.-S. Hong, “Bundled-optode implementation for 3D imaging in functional near-infrared spectroscopy, ” Biomed. Opt. Exp. 7, 3491–3507 (2016).

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

    Xiaolong Liu, Keum-Shik Hong. Detection of primary RGB colors projected on a screen using fNIRS[J]. Journal of Innovative Optical Health Sciences, 2017, 10(3): 1750006
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