Skip to main content

Advertisement

SpringerLink
Log in

Brain–machine interfaces using functional near-infrared spectroscopy: a review

  • Invited Article
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

Abstract

Functional near-infrared spectroscopy (fNIRS) is a noninvasive method for acquiring hemodynamic signals from the brain with advantages of portability, affordability, low susceptibility to noise, and moderate temporal resolution that serves as a plausible solution to real-time imaging. fNIRS is an emerging brain imaging technique that measures brain activity by means of near-infrared light of 600–1000 nm wavelengths. Recently, there has been a surge of studies with fNIRS for the acquisition, decoding, and regulation of hemodynamic signals to investigate their behavioral consequences for the implementation of brain–machine interfaces (BMI). In this review, first, the existing methods of fNIRS signal processing for decoding brain commands for BMI purposes are reviewed. Second, recent developments, applications, and challenges faced by fNIRS-based BMIs are outlined. Third, current trends in fNIRS in combination with other imaging modalities are summarized. Finally, we propose a feedback control concept for the human brain, in which fNIRS, electroencephalography, and functional magnetic resonance imaging are considered sensors and stimulation techniques are considered actuators in brain therapy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Wolpaw JR, Birbaumer N, McFarland D, Pfurtscheller G, Vaughan TM (2002) Brain–computer interfaces for communication and control. Clin Neurophysiol 113(6):767–791

    Google Scholar 

  2. Moghimi S, Kushki A, Marie Guerguerian A, Chau T (2013) A review of EEG-based brain–computer interfaces as access pathways for individuals with severe disabilities. Assist Technol 25(2):99–110

    Google Scholar 

  3. Eddy BS, Garrett SC, Rajen S, Peters B, Wiedrick J, McLaughlin D, O’Connor A, Renda A, Huggins JE, Fried-Oken M (2019) Trends in research participant categories and descriptions in abstracts from the international BCI meeting series, 1999 to 2016. Brain Comput Interface 6(1–2):13–24

    Google Scholar 

  4. https://www.un.org/en/development/desa/population/publications/pdf/ageing/WPA2015_Report.pdf. Accessed Dec 2016

  5. Nicolas-Alonso LF, Gomez-Gil J (2012) Brain–computer interfaces, a review. Sensors 12(2):1211–1279

    Google Scholar 

  6. Naseer N, Hong KS (2015) fNIRS-based brain–computer interfaces: a review. Front Hum Neurosci 9:3

    Google Scholar 

  7. Birbaumer N (2006) Brain computer-interface research: coming of age. Clin Neurophysiol 117(3):479–483

    Google Scholar 

  8. Birbaumer N, Cohen LG (2007) Brain computer interfaces: communication and restoration of movement in paralysis. J Physiol 579:621–636

    Google Scholar 

  9. Sitaram R, Caria A, Birbaumer N (2009) Hemodynamic brain–computer interfaces for communication and rehabilitation. IEEE Trans Neural Netw Learn Syst 22(9):1320–1328

    Google Scholar 

  10. Min BK, Marzelli MJ, Yoo SS (2010) Neuroimaging-based approaches in brain–computer interface. Trends Biotechnol 28:552–560

    Google Scholar 

  11. Hong KS, Khan MJ, Hong MJ (2018) Feature extraction and classification methods for hybrid fNIRS-EEG brain–computer interfaces. Front Hum Neurosci 12:246

    Google Scholar 

  12. Birbaumer N, Ghanayim N, Hinterberger T, Iversen I, Kotchoubey B, Kubler A, Perelmouter J, Taub E, Flor H (1999) A spelling device for the paralysed. Nature 398:297–298

    Google Scholar 

  13. Chapin JK, Moxon KA, Markowitz RS, Nicolelis MA (1999) Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nat Neurosci 2:664–670

    Google Scholar 

  14. Vidal JJ (1973) Toward direct brain–computer communication. Annu Rev Biophys Bioeng 2:157–180

    Google Scholar 

  15. Khan MJ, Hong KS (2015) Passive BCI based on drowsiness detection: an fNIRS study. Biomed Opt Express 6(10):4063–4078

    Google Scholar 

  16. Bashashati A, Fatourechi M, Ward RK, Birch GE (2007) A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals. J Neural Eng 4(2):R32–R57

    Google Scholar 

  17. Lotte F, Congedo M, L´ecuyer A, Lamarche F, Arnaldi B (2007) A review of classification algorithms for EEG-based brain–computer interfaces. J Neural Eng 4(2):R1–R13

    Google Scholar 

  18. Trejo LJ, Rosipal R, Matthews B (2006) Brain–computer interfaces for 1-D and 2-D cursor control: designs using volitional control of the EEG spectrum or steady-state visual evoked potentials. IEEE Trans Neural Syst Rehabil Eng 14:225–229

    Google Scholar 

  19. Wang D, Miao DQ, Blohm G (2012) Multi-class motor imagery EEG decoding for brain–computer interfaces. Front Neurosci 6:151

    Google Scholar 

  20. Turnip A, Hong KS (2012) Classifying mental activites from EEG-P300 signals using adaptive neural network. Int J Innov Comp Inf Control 8(9):6429–6443

    Google Scholar 

  21. Turnip A, Hong KS, Jeong MY (2011) Real-time feature extraction of EEG-based P300 using adaptive nonlinear principal component analysis. Biomed Eng Online 10(83):1–20

    Google Scholar 

  22. Ahn M, Jun SC (2015) Performance variation in motor imagery brain–computer interface: a brief review. J Neurosci Methods 243:103–110

    Google Scholar 

  23. Wang HT, Li YQ, Long JY, Yu TY, Gu ZH (2014) An asynchronous wheelchair control by hybrid EEG-EOG brain–computer interface. Cogn Neurodyn 8:399–409

    Google Scholar 

  24. Ramli R, Arof H, Ibrahim F, Mokhtar N, Idris MYI (2015) Using finite state machine and a hybrid of EEG signal and EOG artefacts for an asynchronous wheelchair navigation. Expert Syst Appl 42:2451–2463

    Google Scholar 

  25. Zhang R, Li YQ, Yan YY, Zhang H, Wu SY, Yu TY, Gu ZH (2016) Control of a wheelchair in an indoor environment based on a brain–computer interface and automated navigation. IEEE Trans Neural Syst Rehabil Eng 24:128–139

    Google Scholar 

  26. Kim BH, Kim M, Jo S (2014) Quadcopter flight control using a low-cost hybrid interface with EEG-based classification and eye tracking. Comput Biol Med 51:82–92

    Google Scholar 

  27. Boas DA, Elwell CE, Ferrari M, Taga G (2014) Twenty years of functional near-infrared spectroscopy: introduction for the special issue. Neuroimage 85:1–5

    Google Scholar 

  28. Liu X, Hong KS (2017) Detection of primary RGB colors projected on a screen using fNIRS. J Innov Opt Health Sci 10:6

    Google Scholar 

  29. Bhutta MR, Hong MJ, Kim YH, Hong KS (2015) Single-trial lie detection using a combined fNIRS-polygraph system. Front Psychol 6:709

    Google Scholar 

  30. Scholkmann F, Kleiser S, Metz AJ, Zimmermann R, Pavia JM, Wolf U, Wolf M (2014) A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology. Neuroimage 85:6–27

    Google Scholar 

  31. Huppert TJ, Hoge RD, Diamond SG, Franceschini MA, Boas DA (2006) A temporal comparison of BOLD, ASL, and NIRS hemodynamic responses to motor stimuli in adult humans. Neuroimage 29(2):368–382

    Google Scholar 

  32. Hu XS, Hong KS, Ge SS, Jeong MY (2010) Kalman estimator- and general linear model-based on-line brain activation mapping by near-infrared spectroscopy. Biomed Eng Online 9:82

    Google Scholar 

  33. Pinti P, Aichelburg C, Gilbert S, Hamilton A, Hirsch J, Burgess P, Tachtsidis I (2018) A review on the use of wearable functional near-infrared spectroscopy in naturalistic environments. Jpn Psychol Res 60(4):347–373

    Google Scholar 

  34. Chaudhary U, Xia B, Silvoni S, Cohen LG, Birbaumer N (2017) Brain–computer interface–based communication in the completely locked-in state. PLoS Biol 15(1):1002593

    Google Scholar 

  35. Gallegos-Ayala G, Furdea A, Takano K, Ruf CA, Flor H, Birbaumer N (2014) Brain communication in a completely locked-in patient using bedside near-infrared spectroscopy. Neurology 82(21):1930–1932

    Google Scholar 

  36. Abdalmalak A, Milej D, Norton L, Debicki D, Gofton T, Diop M, Owen AM, Lawrence KS (2017) Single-session communication with a locked-in patient by functional near-infrared spectroscopy. Neurophotonics 4(4):040501

    Google Scholar 

  37. Fazli S, Mehnert J, Steinbrink J, Curio G, Villringer A, Muller KR, Blankertz B (2012) Enhanced performance by a hybrid NIRS-EEG brain computer interface. Neuroimage 59:519–529

    Google Scholar 

  38. Tomita Y, Vialatte FB, Dreyfus G, Mitsukura Y, Bakardjian H, Cichocki A (2014) Bimodal BCI using simultaneosuly NIRS and EEG. IEEE Trans Biomed Eng 61(4):1274–1284

    Google Scholar 

  39. Khan MJ, Hong KS (2017) Hybird EEG-fNIRS-based eight command decoding for BCI: application to quadcopter control. Front Neurorobotics 11:6

    Google Scholar 

  40. Hong KS, Khan MJ (2017) Hybrid brain–computer interface techniques for improved classification accuracy and increased number of commands: a review. Front Neurorobotics 11:35

    Google Scholar 

  41. Santosa H, Hong MJ, Hong KS (2014) Lateralization of music processing with noises in the auditory cortex: an fNIRS study. Front Behav Neurosci 8:418

    Google Scholar 

  42. Hong KS, Bhutta MR, Liu X, Shin YI (2017) Classification of somatosensory cortex activities using fNIRS. Behav Brain Res 333:225–234

    Google Scholar 

  43. Naseer N, Hong KS (2015) Decoding answers to four-choice questions using functional near-infrared spectroscopy. J Near Infrared Spectrosc 23(1):23–31

    Google Scholar 

  44. Jobsis FF (1977) Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters. Science 198:1264–1267

    Google Scholar 

  45. Coyle SM, Ward TE, Markham CM, McDarby G (2004) On the suitability of near-infrared (NIR) systems for next-generation brain–computer interfaces. Physiol Meas 25(4):815

    Google Scholar 

  46. Coyle SM, Ward TE, Markham CM (2007) Brain–computer interface using a simplified functional near-infrared spectroscopy system. J Neural Eng 4(3):219

    Google Scholar 

  47. Sitaram R, Zhang H, Guan C, Thulasidas M, Hoshi Y, Ishikawa A, Shimizu K, Birbaumer N (2007) Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain–computer interface. Neuroimage 34(4):1416–1427

    Google Scholar 

  48. Naito M, Michioka Y, Ozawa K, Ito Y, Kiguchi M, Kanazawa T (2007) 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 90(7):1028–1037

    Google Scholar 

  49. Utsugi K, Obata A, Sato H, Aoki R, Maki A, Koizumi H, Sagara K, Kawamichi H, Atsumori H, Katura T (2008) GO-STOP control using optical brain–computer interface during calculation task. IEICE Trans Commun 91(7):2133–2141

    Google Scholar 

  50. Bauernfeind G, Leeb R, Wriessnegger SC, Pfurtscheller G (2008) Development, set-up and first results for a one-channel near-infrared spectroscopy system. Biomed Tech 53(1):36–43

    Google Scholar 

  51. Tai K, Chau T (2009) Single-trial classification of NIRS signals during emotional induction tasks: towards a corporeal machine interface. J NeuroEng Rehabil 6(1):39

    Google Scholar 

  52. Luu S, Chau T (2009) Decoding subjective preference from single-trial near-infrared spectroscopy signals. J Neural Eng 6(1):016003

    Google Scholar 

  53. Power SD, Falk TH, Chau T (2010) Classification of prefrontal activity due to mental arithmetic and music imagery using hidden Markov models and frequency domain near-infrared spectroscopy. J Neural Eng 7(2):026002

    Google Scholar 

  54. Cui X, Bray S, Reiss AL (2010) Speeded near infrared spectroscopy (NIRS) response detection. PLoS ONE 5(11):15474

    Google Scholar 

  55. Coffey EB, Brouwer AM, Wilschut ES, van Erp JB (2010) Brain–machine interfaces in space: using spontaneous rather than intentionally generated brain signals. Acta Astronaut 67(1–2):1–11

    Google Scholar 

  56. Power SD, Khushki A, Chau T (2012) Automatic single-trial discrimination of mental arithmetic, mental singing and no-control state form the prefrontal activity: towards the three state NIRS-BCI. BMC Res Notes 5:141

    Google Scholar 

  57. Pfurtscheller G, Allison BZ, Bauernfeind G, Brunner C, Solis Escalante T, Scherer R, Zander TO, Mueller-Putz G, Neuper C, Birbaumer N (2010) The hybrid BCI. Front Neurosci 4:3

    Google Scholar 

  58. Hong KS, Zafar A (2018) Existence of initial dip for BCI: an illusion or reality. Front Neurorobotics 12:69

    Google Scholar 

  59. Misawa T, Takano S, Shimokawa T, Hirobayashi S (2012) A brain–computer interface for motor assist by the prefrontal cortex. Electron Commun Jp 95(10):1–8

    Google Scholar 

  60. McFarland DJ, Wolpaw JR (2011) Brain–computer interfaces for communication and control. Commun ACM 54:660–666

    Google Scholar 

  61. Hong KS, Santosa H (2016) Decoding four different sound-categories in the auditory cortex using functional near-infrared spectroscopy. Hear Res 333:157–166

    Google Scholar 

  62. Naseer N, Noori FM, Qureshi NK, Hong KS (2016) Determining optimal feature-combination for LDA classification of functional near-infrared spectroscopy signals in brain–computer interface application. Front Hum Neurosci 10:237

    Google Scholar 

  63. Pinti P, Cardone D, Merla A (2015) Simultaneous fNIRS and thermal infrared imaging during cognitive task reveal autonomic correlates of prefrontal cortex activity. Sci Rep 5:17471

    Google Scholar 

  64. Abibullaev B, An J, Moon JI (2011) Neural network classification of brain hemodynamic responses from four mental tasks. Int J Optomechatronics 5(4):340–359

    Google Scholar 

  65. Abibullaev B, An J (2012) Classification of frontal cortex hemodynamic responses during cognitive tasks using wavelet transforms and machine learning algorithms. Med Eng Phys 34(10):1394–1410

    Google Scholar 

  66. Holper L, Wolf M (2011) Single-trial classification of motor imagery differing in task complexity: a functional near-infrared spectroscopy study. J Neuroeng Rehabil 8:34

    Google Scholar 

  67. Tanaka H, Katura T (2011) Classification of change detection and change blindness from near-infrared spectroscopy signals. J Biomed Opt 16(8):087001

    Google Scholar 

  68. Bauernfeind G, Scherer R, Pfurtscheller G, Neuper C (2011) Single-trial classification of antagonistic oxyhemoglobin responses during mental arithmetic. Med Biol Eng Comput 49(9):979–984

    Google Scholar 

  69. Power SD, Kushki A, Chau T (2011) 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

    Google Scholar 

  70. Chan J, Power S, Chau T (2012) Investigating the need for modeling temporal dependencies in a brain–computer interface with real-time feedback based on near infrared spectra. J Near Infrared Spectrosc 20(1):107–116

    Google Scholar 

  71. Seo Y, Lee S, Koh D, Kim BM (2012) Partial least squares-discriminant analysis for the prediction of hemodynamic changes using near-infrared spectroscopy. J Opt Soc Korea 16(1):57–62

    Google Scholar 

  72. Power SD, Kushki A, Chau T (2012) Intersession consistency of single-trial classification of the prefrontal response to mental arithmetic and the no-control state by NIRS. PLoS ONE 7(7):37791

    Google Scholar 

  73. Falk TH, Guirgis M, Power S, Chau TT (2011) Taking NIRS-BCIs outside the lab: towards achieving robustness against environment noise. IEEE Trans Neural Syst Rehabil Eng 19(2):136–146

    Google Scholar 

  74. Stangl M, Bauernfeind G, Kurzmann J, Scerer R, Neuper C (2013) A hemodynamic brain–computer interface based on real-time classification of near infrared spectroscopy signals during motor imagery and mental arithmetic. J Near Infrared Spectrosc 21(3):157–171

    Google Scholar 

  75. Naseer N, Hong KS (2013) Classification of functional near-infrared spectroscopy signals corresponding to right- and left-wrist motor imagery for development of a brain–computer interface. Neurosci Lett 553:84–89

    Google Scholar 

  76. Zimmermann R, Marchal-Crespo L, Edelmann J, Lambercy O, Fluet MC, Riener R, Wolf M, Gassert R (2013) Detection of motor execution using hybrid fNIRS-biosignal BCI: a feasibility study. J Neuroeng Rehabil 10:4

    Google Scholar 

  77. Hai NT, Cuong NQ, Khoa TQD, Toi VV (2013) Temporal hemodynamic classification of two hands tapping using functional near-infrared spectroscopy. Front Hum Neurosci 7:516

    Google Scholar 

  78. Faress A, Chau T (2013) Towards a multimodal brain–computer interface: combining fNIRS and fTCD measurements to enable higher classification accuracy. Neuroimage 77:186–194

    Google Scholar 

  79. Moghimi S, Kushki A, Power S, Guerguerian AM, Chau T (2012) Automatic detection of a prefrontal cortical response to emotionally rated music using multi-channel near-infrared spectroscopy. J Neural Eng 9(2):026022

    Google Scholar 

  80. Hatakenaka M, Miyai I, Mihara M, Sakoda S, Kubota K (2007) Frontal regions involved in learning of motor skill—a functional NIRS study. Neuroimage 34:109–116

    Google Scholar 

  81. Weyand S, Chau T (2015) Correlates of near-infrared spectroscopy brain–computer interface accuracy in a multi-class personalization framework. Front Hum Neurosci 9:536

    Google Scholar 

  82. Zander TO, Kothe C (2011) Towards passive brain–computer interfaces: applying brain–computer interface technology to human-machine systems in general. J Neural Eng 8:025005

    Google Scholar 

  83. Jurcak V, Tsuzuki D, Dan I (2007) 10/20, 10/10, and 10/5 system revisited: their validity as head-surface-based positioning system. Neuroimage 34:1600–1611

    Google Scholar 

  84. Tsuzuki D, Dan I (2014) Spatial registration for functional near-infrared spectroscopy: from channel position on the scalp to cortical location in individual and group analyses. Neuroimage 85:92–103

    Google Scholar 

  85. Gratton G, Brumback CR, Gordon BA, Pearson MA, Low KA, Fabiani M (2006) Effects of measurement method, wavelength, and source-detector distance on the fast optical signal. Neuroimage 32(4):1576–1590

    Google Scholar 

  86. Hu XS, Hong KS, Ge SS (2012) fNIRS-based online deception decoding. J Neural Eng 9(2):026012

    Google Scholar 

  87. Nguyen HD, Hong KS, Shin YI (2016) Bundled-optode method in functional near-infrared spectroscopy. PLoS ONE 11(10):0165146

    Google Scholar 

  88. Yücel MA, Selb J, Aasted CM, Petkov MP, Becerra L, Borsook D, Boas DA (2015) Short separation regression improves statistical significance and better localizes the hemodynamic response obtained by near-infrared spectroscopy for tasks with differing autonomic responses. Neurophotonics 2(3):035005

    Google Scholar 

  89. Hirasawa A, Kaneko T, Tanaka N, Funane T, Kiguchi M, Sørensen H, Secher NH, Ogoh S (2016) Near-infrared spectroscopy determined cerebral oxygenation with eliminated skin blood flow in young males. J Clin Monitor Comp 30(2):243–250

    Google Scholar 

  90. Brigadoi S, Cooper RJ (2015) How short is short? Optimum source–detector distance for short-separation channels in functional near-infrared spectroscopy. Neurophotonics 2(2):025005

    Google Scholar 

  91. Gao L, Cai Y, Wang H, Wang G, Zhang Q, Yan X (2019) Probing prefrontal cortex hemodynamic alterations during facial emotion recognition for major depression disorder through functional near-infrared spectroscopy. J Neural Eng 16(2):026026

    Google Scholar 

  92. Khan MJ, Hong MJ, Hong KS (2014) Decoding of four movement directions using hybrid NIRS-EEG brain–computer interface. Front Hum Neurosci 8:244

    Google Scholar 

  93. Aqil M, Hong KS, Jeong MY, Ge SS (2012) Detection of event-related hemodynamic response to neuroactivation by dynamic modeling of brain activity. Neuroimage 63(1):553–568

    Google Scholar 

  94. Naseer N, Qureshi NK, Noori FM, Hong KS (2016) Analysis of different classification techniques for two-class functional near-infrared spectroscopy based brain–computer interface. Comput Intell Neurosci 2016:5480760

    Google Scholar 

  95. Pinti P, Scholkmann F, Hamilton A, Burgess P, Tachtsidis I (2018) Current status and issues regarding pre-processing of fNIRS neuroimaging data: an investigation of diverse signal filtering methods within a general linear model framework. Front Hum Neurosci 12:505

    Google Scholar 

  96. Tachtsidis I, Scholkmann F (2016) False positives and false negatives in functional near-infrared spectroscopy: issues, challenges, and the way forward. Neurophotonics 3(3):031405

    Google Scholar 

  97. Bauernfeind G, Wriessnegger SC, Daly I, Müller-Putz GR (2014) Separating heart and brain: on the reduction of physiological noise from multichannel functional near-infrared spectroscopy (fNIRS) signals. J Neural Eng 11(5):056010

    Google Scholar 

  98. Cooper RJ, Selb J, Gagnon L, Phillip D, Schytz HW, Iversen HK, Ashina M, Boas DA (2012) A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy. Front Neurosci 6:147

    Google Scholar 

  99. Ganjefar S, Afshar M, Sarajchi MH, Shao Z (2018) Controller design based on wavelet neural adaptive proportional plus conventional integral-derivative for bilateral teleoperation systems with time-varying parameters. Int J Control Autom Syst 16(5):2405–2420

    Google Scholar 

  100. Huppert TJ, Diamond SG, Fransceshini MA, Boas DA (2009) HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain. Appl Opt 48(10):D280–D298

    Google Scholar 

  101. Hu XS, Hong KS, Ge SS (2011) Recognition of stimulus-evoked neuronal optical response by identifying chaos levels of near-infrared spectroscopy time series. Neurosci Lett 504(2):115–120

    Google Scholar 

  102. Zhu T, Zhou Y, Xia Z, Dong J, Zhao Q (2018) Progressive filtering approach for early human action recognition. Int J Control Autom Syst 16(5):2393–2404

    Google Scholar 

  103. Santosa H, Hong MJ, Kim SP, Hong KS (2013) Noise reduction in functional near-infrared spectroscopy signals by independent component analysis. Rev Sci Instrum 84(7):073106

    Google Scholar 

  104. Nguyen QC, Piao M, Hong KS (2018) Multivariable adaptive control of the rewinding process of a roll-to-roll system governed by hyperbolic partial differential equations. Int J Control Autom Syst 16(5):2177–2186

    Google Scholar 

  105. Schudlo LC, Chau T (2014) Dynamic topographical pattern classification of multichannel prefrontal NIRS signals: II Online differentiation of mental arithmetic and rest. J Neural Eng 11:016003

    Google Scholar 

  106. Naseer N, Hong MJ, Hong KS (2014) Online binary decision decoding using functional near-infrared spectroscopy for the development of brain–computer interface. Exp Brain Res 232(2):555–564

    Google Scholar 

  107. Shin J, Jeong J (2014) Multiclass classification of hemodynamic responses for performance improvement of functional near-infrared spectroscopy-based brain–computer interface. J Biomed Opt 19:067009

    Google Scholar 

  108. Hwang HJ, Lim JH, Kim DW, Im CH (2014) Evaluation of various mental task combinations for near-infrared spectroscopy-based brain–computer interfaces. J Biomed Opt 19(7):077005

    Google Scholar 

  109. Hong KS, Naseer N, Kim YH (2015) Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI. Neurosci Lett 587:87–92

    Google Scholar 

  110. Mihara M, Miyai I, Hattori N, Hatakenaka M, Yagura H, Kawano T, Okibayashi M, Danjo N, Ishikawa A, Inoue Y, Kubota K (2012) Neurofeedback using real-time near-infrared spectroscopy enhances motor imagery related cortical activation. PLoS ONE 7(3):32234

    Google Scholar 

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

    Google Scholar 

  112. Noori FM, Naseer N, Qureshi NK, Nazeer H, Khan RA (2017) Optimal feature selection from fNIRS signals using genetic algorithms for BCI. Neurosci Lett 647:61–66

    Google Scholar 

  113. Yin X, Xu B, Jiang C, Fu Y, Wang Z, Li H, Shi G (2015) NIRS-based classification of clench force and speed motor imagery with the use of empirical mode decomposition for BCI. Med Eng Phys 37(3):280–286

    Google Scholar 

  114. Gateau T, Durantin G, Lancelot F, Scannella S, Dehais F (2015) Real-time state estimation in a flight simulator using fNIRS. PLoS ONE 10(3):0121279

    Google Scholar 

  115. Hong KS, Naseer N (2016) 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

    Google Scholar 

  116. Zafar A, Hong KS (2017) Detection and classification of three-class initial dips from prefrontal cortex. Biomed Opt Express 8:367–383

    Google Scholar 

  117. Yin X, Xu B, Jiang C, Fu Y, Wang Z, Li H, Shi G (2015) Classification of hemodynamic responses associated with force and speed imagery for a brain–computer interface. J Med Syst 39(5):53

    Google Scholar 

  118. Pamosoaji AK, Piao M, Hong KS (2019) PSO-based minimum-time motion planning for multiple vehicles under acceleration and velocity limitations. Int J Control Autom Syst 17(10):2610–2623

    Google Scholar 

  119. Cavazza M, Aranyi G, Charles F (2017) BCI control of heuristic search algorithms. Front Neuroinformatics 11:6

    Google Scholar 

  120. Hwang HJ, Choi H, Kim JY, Chang WD, Kim DW, Kim K, Jo S, Im CH (2016) Toward more intuitive brain–computer interfacing: classification of binary covert intentions using functional near-infrared spectroscopy. J Biomed Opt 21(9):091303

    Google Scholar 

  121. Watanabe K, Tanaka H, Takahashi K, Niimura Y, Watanabe KY (2016) NIRS-based language learning BCI system. IEEE Sens J 16(8):2726–2734

    Google Scholar 

  122. Liu Y, Ayaz H (2018) Speech recognition via fNIRS based brain signals. Front Neurosci 12:695

    Google Scholar 

  123. Sereshkeh AR, Yousefi R, Wong AT, Chau T (2019) Online classification of imagined speech using functional near-infrared spectroscopy signals. J Neural Eng 16(1):016005

    Google Scholar 

  124. Abibullaev B, An J, Jin SH, Moon JI (2014) Classification of brain hemodynamic signals arising from visual action observation tasks for brain–computer interfaces: a functional near-infrared spectroscopy study. Measurement 49:320–328

    Google Scholar 

  125. Abibullaev B, An J, Lee SH, Moon JI (2017) Design and evaluation of action observation and motor imagery based BCIs using near-infrared spectroscopy. Measurement 98:250–261

    Google Scholar 

  126. Mihara M, Hattori N, Hatakenaka M, Yagura H, Kawano T, Hino T, Miyai I (2013) Near-infrared spectroscopy–mediated neurofeedback enhances efficacy of motor imagery–based training in poststroke victims a pilot study. Stroke 44(4):1091–1098

    Google Scholar 

  127. Lapborisuth P, Zhang X, Noah A, Hirsch J (2017) Neurofeedback-based functional near-infrared spectroscopy upregulates motor cortex activity in imagined motor tasks. Neurophotonics 4(2):021107

    Google Scholar 

  128. Aranyi G, Pecune F, Charles F, Pelachaud C, Cavazza M (2016) Affective interaction with a virtual character through an fNIRS brain–computer interface. Front Comput Neurosci 10:70

    Google Scholar 

  129. Luhrs M, Goebel R (2017) Turbo-Satori: a neurofeedback and brain–computer interface tool box for real-time functional near-infrared spectroscopy. Neurophotonics 4(4):041504

    Google Scholar 

  130. Batula AM, Kim YE, Ayaz H (2017) Virtual and actual humanoid robot control with four-class motor-imagery-based optical brain–computer interface. Biomed Res Int 2017:1463512

    Google Scholar 

  131. Wyser DG, Lambercy O, Scholkmann F, Wolf M, Gassert R (2017) Wearable and modular functional near-infrared spectroscopy instrument with multidistance measurements at four wavelengths. Neurophotonics 4(4):041413

    Google Scholar 

  132. Shin J, Kwon J, Choi J, Im CH (2018) Ternary near-infrared spectroscopy brain–computer interface with increased information transfer rate using prefrontal hemodynamic changes during mental arithmetic, breath-holding, and idle state. IEEE Access 6:19491–19498

    Google Scholar 

  133. Shin J, Kim DW, Müller KR, Hwang HJ (2018) Improvement of information transfer rates using a hybrid EEG-NIRS brain–computer interface with a short trial length: offline and pseudo-online analyses. Sensors 18(6):1827

    Google Scholar 

  134. Hong KS, Pham PT (2019) Control of axially moving systems: a review. Int J Control Autom Syst 17(12):2983–3008

    Google Scholar 

  135. Li Z, Jiang YH, Duan L, Zhu CZ (2017) A Gaussian mixture model based adaptive classifier for fNIRS brain–computer interfaces and its testing via simulation. J Neural Eng 14(4):046014

    Google Scholar 

  136. Zhang S, Zheng Y, Wang D, Wang L, Ma J, Zhang J, Xu W, Li D, Zhang D (2017) Application of a common spatial pattern-based algorithm for an fNIRS-based motor imagery brain–computer interface. Neurosci Lett 655:35–40

    Google Scholar 

  137. Kaiser V, Bauernfeind G, Kreilinger A, Kaufmann T, Kubler A, Neuper C, Muller-Putz GR (2014) Cortical effects of user training in a motor imagery based brain–computer interface measured by fNIRS and EEG. Neuroimage 85:432–444

    Google Scholar 

  138. Blokland Y, Spyrou L, Thijssen D, Eijsvogels T, Colier W, Floor-Westerdijk M, Vlek R, Bruhn J, Farquhar J (2014) Combined EEG-fNIRS decoding of motor attempt and imagery for brain switch control: an offline study in patients with tetraplegia. IEEE Trans Neural Syst Rehabil Eng 22:222–229

    Google Scholar 

  139. Putze F, Hesslinger S, Tse CY, Huang YY, Herff C, Guan CT, Schultz T (2014) Hybrid fNIRS-EEG based classification of auditory and visual perception processes. Front Neurosci 8:373

    Google Scholar 

  140. Morioka H, Kanemura A, Morimoto S, Yoshioka T, Oba S, Kawanabe M, Ishii S (2014) Decoding spatial attention by using cortical currents estimated from electroencephalography with near-infrared spectroscopy prior information. Neuroimage 90:128–139

    Google Scholar 

  141. Koo B, Lee HG, Nam Y, Kang H, Koh CS, Shin HC, Choi S (2015) A hybrid NIRS-EEG system for self-paced brain computer interface with online motor imagery. J Neurosci Methods 244:26–32

    Google Scholar 

  142. Yin XX, Xu BL, Jiang CH, Fu YF, Wang ZD, Li HY, Shi G (2015) A hybrid BCI based on EEG and fNIRS signals improves the performance of decoding motor imagery of both force and speed of hand clenching. J Neural Eng 12:036004

    Google Scholar 

  143. Lee MH, Fazli S, Mehnert J, Lee SW (2015) Subject-dependent classification for robust idle state detection using multi-modal neuroimaging and data-fusion techniques in BCI. Pattern Recognit 48:2725–2737

    Google Scholar 

  144. Buccino AP, Keles HO, Omurtag A (2016) Hybrid EEG-fNIRS asynchronous brain–computer interface for multiple motor tasks. PLoS ONE 11:0146610

    Google Scholar 

  145. Ahn S, Nguyen T, Jang H, Kim JG, Jun SC (2016) Exploring neuro-physiological correlates of drivers’ mental fatigue caused by sleep deprivation using simultaneous EEG, ECG, and fNIRS data. Front Hum Neurosci 10:219

    Google Scholar 

  146. Li R, Potter T, Huang W, Zhang Y (2017) Enhancing performance of a hybrid EEG-FNIRS system using channel selection and early temporal features. Front Hum Neurosci 11:462

    Google Scholar 

  147. Aghajani H, Garbey M, Omurtag A (2017) Measuring mental workload with EEG plus fNIRS. Front Hum Neurosci 11:359

    Google Scholar 

  148. Liu Y, Ayaz H, Shewokis PA (2017) Mental workload classification with concurrent electroencephalography and functional near-infrared spectroscopy. Brain–Computer Interfaces 4(3):175–185

    Google Scholar 

  149. Shin J, Müller KR, Schmitz CH, Kim DW, Hwang HJ (2017) Evaluation of a compact hybrid brain–computer interface system. Biomed Res Int 2017:6820482

    Google Scholar 

  150. Omurtag A, Aghajani H, Keles HO (2017) Decoding human mental states by whole-head EEG+ FNIRS during category fluency task performance. J Neural Eng 14(6):066003

    Google Scholar 

  151. Zhang M, Hua Q, Jia W, Chen R, Su H, Wang B (2018) Feature extraction and classification algorithm of brain–computer interface based on human brain central nervous system. NeuroQuantology 16(5):896–900

    Google Scholar 

  152. Chiarelli AM, Croce P, Merla A, Zappasodi F (2018) Deep learning for hybrid EEG-fNIRS brain–computer interface: application to motor imagery classification. J Neural Eng 15(3):036028

    Google Scholar 

  153. Zafar A, Hong KS (2018) Neuronal activation detection using vector phase analysis with dual threshold circles: a functional near-infrared spectroscopy study. Int J Neural syst 28(10):1850031

    Google Scholar 

  154. Erdoğan SB, Özsarfati E, Dilek B, Kadak KS, Hanoğlu L, Akin A (2019) Classification of motor imagery and execution signals with population-level feature sets: implications for probe design in fNIRS based BCI. J Neural Eng 16:026029

    Google Scholar 

  155. Khan MJ, Ghafoor U, Hong KS (2018) Early detection of hemodynamic responses using EEG: a hybrid EEG-fNIRS study. Front Hum Neurosci 12:479

    Google Scholar 

  156. Yaqub MA, Woo SW, Hong KS (2018) Effects of HD-tDCS on resting-state functional connectivity in the prefrontal cortex: an fNIRS study. Complexity 2018:1613402

    Google Scholar 

  157. Ghafoor U, Lee JH, Hong KS, Park SS, Kim J, Yoo HR (2019) Effects of acupuncture therapy on MCI patients using functional near-infrared spectroscopy. Front Aging Neurosci 11:237

    Google Scholar 

  158. Hong KS, Yaqub MA (2019) Application of functional near-infrared spectroscopy in the health industry: a review. J Innov Opt Health Sci 12(6):1930012

    Google Scholar 

  159. Yang D, Hong KS, Yoo SH, Kim CS (2019) Evaluation of neural degeneration biomarkers in the prefrontal cortex for early identification of patients with mild cognitive impairment: an fNIRS study. Front Hum Neurosci 13:317

    Google Scholar 

  160. Bhutta MR, Hong KS, Kim BM, Hong MJ, Kim YH, Lee SH (2014) Note: three wavelengths near-infrared spectroscopy system for compensating the light absorbance by water. Rev Sci Instrum 85:026111

    Google Scholar 

  161. Curtin A, Ayaz H (2018) The age of neuroergonomics: towards ubiquitous and continuous measurement of brain function with fNIRS. Jpn Psychol Res 60(4):374–386

    Google Scholar 

  162. Yi G, Mao JX, Wang YN, Guo SY, Miao ZQ (2018) Adaptive tracking control of nonholonomic mobile manipulators using recurrent neural networks. Int J Control Autom Syst 16(3):1390–1403

    Google Scholar 

  163. Petrantonakis PC, Kompatsiaris I (2018) Single-trial NIRS data classification for brain–computer interfaces using graph signal processing. IEEE Trans Neural Syst Rehabil Eng 26(9):1700–1709

    Google Scholar 

  164. Kazemy A, Cao J (2018) Consecutive synchronization of a delayed complex dynamical network via distributed adaptive control approach. Int J Control Autom Syst 16(6):2656–2664

    Google Scholar 

  165. Nguyen HD, Hong KS (2016) Bundled optode implementation of 3D imaging in functional near-infrared spectroscopy. Biomed Opt Express 7(9):3419–3507

    Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Research Foundation (NRF) of Korea under the auspices of the Ministry of Science and ICT, Republic of Korea (Grant no. NRF-2017R1A2A1A17069430).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Pusan National University, 2 Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan, 46241, Republic of Korea

    Keum-Shik Hong & Usman Ghafoor

  2. School of Mechanical and Manufacturing Engineering, National University of Science and Technology, Islamabad, 44000, Pakistan

    M. Jawad Khan

Authors
  1. Keum-Shik Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Usman Ghafoor
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Jawad Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Keum-Shik Hong.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest. This research was conducted in the absence of any commercial or financial relationship that could be construed as a potential conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hong, KS., Ghafoor, U. & Khan, M.J. Brain–machine interfaces using functional near-infrared spectroscopy: a review. Artif Life Robotics 25, 204–218 (2020). https://doi.org/10.1007/s10015-020-00592-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-020-00592-9

Keywords

Search

Navigation