Journal of Innovative Optical Health Sciences, 2017, 10 (3): 1750006, Published Online: Dec. 27, 2018   

Detection of primary RGB colors projected on a screen using fNIRS

Author Affiliations
1 School of Mechanical Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 46241, Republic of Korea
2 School of Mechanical Engineering and Department of Cogno-Mechatronics Engineering, Pusan National University
3 2 Busandaehak-ro, Geumjeong-gu, Busan 46241, Republic of Korea
Abstract
In this study, functional near-infrared spectroscopy (fNIRS) is utilized to measure the hemodynamic responses (HRs) in the visual cortex of 14 subjects (aged 22–34 years) viewing the primary red, green, and blue (RGB) colors displayed on a white screen by a beam projector. The spatiotemporal characteristics of their oxygenated and deoxygenated hemoglobins (HbO and HbR) in the visual cortex are measured using a 15-source and 15-detector optode configuration. To see whether the activation maps upon RGB-color stimuli can be distinguished or not, the t-values of individual channels are averaged over 14 subjects. To find the best combination of two features for classification, the HRs of activated channels are averaged over nine trials. The HbO mean, peak, slope, skewness and kurtosis values during 2–7 s window for a given 10 s stimulation period are analyzed. Finally, the linear discriminant analysis (LDA) for classifying three classes is applied. Individually, the best classification accuracy obtained with slope-skewness features was 74.07% (Subject 1), whereas the best overall over 14 subjects was 55.29% with peak-skewness combination. Noting that the chance level of 3-class classification is 33.33%, it can be said that RGB colors can be distinguished. The overall results reveal that fNIRS can be used for monitoring purposes of the HR patterns in the human visual cortex.
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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