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Recent functional near infrared spectroscopy based brain computer interface systems: Developments, applications and challenges

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Abstract

Functional Near Infrared Spectroscopy (fNIRS) based Brain Computer Interface (BCI) systems have grown in popularity in the last years, and has shown itself as a useful tool in developing portable and convenient BCI systems. The purpose of this review paper is to highlight the recent developments, applications, and challenges that research groups have achieved in the field of fNIRS-BCI. We will show how fNIRS can be paired with another modality (i.e. EEG, fTCD, etc.) to drastically improve classification accuracy. From there, we will discuss the recent achievements in classification techniques researchers have had with fNIRS or a combined fNIRS modality. Finally, we will look at how fNIRS-BCI systems are used to enhance human-robot interactions and assistive technologies. Throughout our review paper, we will note challenges groups have had with their studies, as to provide a framework for future research topics for the fNIRS-BCI community.

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References

  1. Ferrari M, Quaresima V. A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application. NeuroImage. 2012; 63(2):921–35.

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Villringer A, Chance B. Non-invasive optical spectroscopy and imaging of human brain function. Trends Neurosci. 1997; 20(10):435–42.

    Article  Google Scholar 

  4. Buxton RB, Frank, LR. A model for the coupling between cerebral blood flow and oxygen metabolism during neural stimulation. J Cereb Blood Flow Metab. 1997; 17(1):64–72.

    Article  Google Scholar 

  5. Cui X, Bray S, Reiss AL. Functional near infrared spectroscopy (NIRS) signal improvement based on negative correlation between oxygenated and deoxygenated hemoglobin dynamics. NeuroImage. 2010; 49(4):3039–46.

    Article  Google Scholar 

  6. Tak S, Ye JC. Statistical analysis of fNIRS data: a comprehensive review. NeuroImage. 2014; 85(Pt 1):72–91.

    Article  Google Scholar 

  7. Derosière G, Mandrick K, Dray G, Ward TE, Perrey S. NIRSmeasured prefrontal cortex activity in neuroergonomics: strengths and weaknesses. Front Hum Neurosci. 2013; doi:10.3389/fnhum.2013.00583.

    Google Scholar 

  8. Hoshi Y, Tamura M. Near-infrared optical detection of sequential brain activation in the prefrontal cortex during mental tasks. NeuroImage. 1997; 5(4 Pt 1):292–7.

    Article  Google Scholar 

  9. Doi H, Nishitani S, Shinohara K. NIRS as a tool for assaying emotional function in the prefrontal cortex. Front Hum Neurosci. 2013; doi:10.3389/fnhum.2013.00770.

    Google Scholar 

  10. Scholkmann F, Kleiser S, Metz AJ, Zimmermann R, Mata Pavia J, Wolf U, Wolf M. A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology. NeuroImage. 2014; 85(Pt 1):6–27.

    Article  Google Scholar 

  11. Tian F, Yennu A, Smith-Osborne A, Gonzalez-Lima F, North CS, Liu H. Prefrontal responses to digit span memory phases in patients with post-traumatic stress disorder (PTSD): a functional near infrared spectroscopy study. NeuroImage Clin. 2014; 4:808–19.

    Article  Google Scholar 

  12. Chou PH, Koike S, Nishimura Y, Kawasaki S, Satomura Y, Kinoshita A, Takizawa R, Kasai K. Distinct effects of duration of untreated psychosis on brain cortical activities in different treatment phases of schizophrenia: a multi-channel near-infrared spectroscopy study. Prog Neuropsychopharmacol Biol Psychiatry. 2014; 49:63–9.

    Article  Google Scholar 

  13. Kito H, Ryokawa A, Kinoshita Y, Sasayama D, Sugiyama N, Ogihara T, Yasaki T, Hagiwara T, Inuzuka S, Takahashi T, Genno H, Nose H, Hanihara T, Washizuka S, Amano N. Comparison of alterations in cerebral hemoglobin oxygenation in late life depression and Alzheimer’s disease as assessed by near-infrared spectroscopy. Behav Brain Funct. 2014; doi:10.1186/1744-9081-10-8.

    Google Scholar 

  14. Araki A, Ikegami M, Okayama A, Matsumoto N, Takahashi S, Azuma H, Takahashi M. Improved prefrontal activity in AD/HD children treated with atomoxetine: a NIRS study. Brain Dev. 2014; doi:10.1016/j.braindev.2014.03.011.

    Google Scholar 

  15. Pfurtscheller G, Flotzinger D, Kalcher J. Brain-computer interface-a new communication device for handicapped persons. J Microcomput Appl. 1993; 16(3):293–9.

    Article  Google Scholar 

  16. Nijboer F, Sellers EW, Mellinger J, Jordan MA, Matuz T, Furdea A, Halder S, Mochty U, Krusienski DJ, Vaughan TM, Wolpaw JR, Birbaumer N, Kübler A. A P300-based braincomputer interface for people with amyotrophic lateral sclerosis. Clin Neurophysiol. 2008; 119(8):1909–16.

    Article  Google Scholar 

  17. Min BK, Marzelli MJ, Yoo SS. Neuroimaging-based approaches in the brain-computer interface. Trends Biotechnol. 2010; 28(11):552–60.

    Article  Google Scholar 

  18. Pfurtscheller G, Allison BZ, Brunner C, Bauernfeind G, Solis-Escalante T, Scherer R, Zander TO, Mueller-Putz G, Neuper C, Birbaumer N. The hybrid BCI. Front Neurosci. 2010; doi:10.3389/fnpro.2010.00003.

    Google Scholar 

  19. Coyle S, Ward T, Markham C, McDarby G. On the suitability of near-infrared (NIR) systems for next-generation brain-computer interfaces. Physiol Meas. 2004; 25(4):815–22.

    Article  Google Scholar 

  20. Coyle SM, Ward TE, Markham CM. Brain-computer interface using a simplified functional near-infrared spectroscopy system. J Neural Eng. 2007; 4(3):219–26.

    Article  Google Scholar 

  21. Naseer N, Hong MJ, Hong KS. Online binary decision decoding using functional near-infrared spectroscopy for the development of brain-computer interface. Exp Brain Res. 2014; 232(2):555–64.

    Article  Google Scholar 

  22. Gallegos-Ayala G, Furdea A, Takano K, Ruf CA, Flor H, Birbaumer N. Brain communication in a completely locked-in patient using bedside near-infrared spectroscopy. Neurology. 2014; 82(21):1930–2.

    Article  Google Scholar 

  23. Birbaumer N, Gallegos-Ayala G, Wildgruber M, Silvoni S, Soekadar SR. Direct brain control and communication in paralysis. Brain Topogr. 2014; 27(1):4–11

    Article  Google Scholar 

  24. Tomita Y, Vialatte FB, Dreyfus G, Mitsukura Y, Bakardjian H, Cichocki A. Bimodal BCI using simultaneously NIRS and EEG. IEEE T Biomed Eng. 2014; 61(4):1274–84.

    Article  Google Scholar 

  25. Cooper RJ, Everdell NL, Enfield LC, Gibson AP, Worley A, Hebden JC. Design and evaluation of a probe for simultaneous EEG and near-infrared imaging of cortical activation. Phys Med Biol. 2009; 54(7):2093–102.

    Article  Google Scholar 

  26. Lareau E, Lesage F, Pouliot P, Nguyen D, Le Lan J, Sawan M. Multichannel wearable system dedicated for simultaneous electroencephalography/near-infrared spectroscopy real-time data acquisitions. J Biomed Opt. 2011; doi:10.1117/1.3625575.

    Google Scholar 

  27. Safaie J, Grebe R, Moghaddam HA, Wallois F. Toward a fully integrated wireless wearable EEG-NIRS bimodal acquisition system. J Neural Eng. 2013; doi:10.1088/1741-2560/10/5/056001.

    Google Scholar 

  28. Fazli S, Mehnert J, Steinbrink J, Curio G, Villringer A, Müller KR, Blankertz B. Enhanced performance by a hybrid NIRSEEG brain computer interface. NeuroImage. 2012; 59(1):519–29.

    Article  Google Scholar 

  29. Koo B, Lee HG, Nam Y, Kang H, Koh CS, Shin HC, Choi S. A hybrid NIRS-EEG system for self-paced brain computer interface with online motor imagery. J Neurosci Methods. 2014; doi:10.1016/j.jneumeth.2014.04.016.

    Google Scholar 

  30. Power SD, Kushki A, Chau T. 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. 2012; doi:10.1186/1756-0500-5-141.

    Google Scholar 

  31. Zimmermann R, Marchal-Crespo L, Edelmann J, Lambercy O, Fluet MC, Riener R, Wolf M, Gassert R. Detection of motor execution using a hybrid fNIRS-biosignal BCI: a feasibility study. J Neuroeng Rehabil. 2013; doi:10.1186/1743-0003-10-4.

    Google Scholar 

  32. Faress A, Chau T. Towards a multimodal brain-computer interface: combining fNIRS and fTCD measurements to enable higher classification accuracy. NeuroImage. 2013; 77:186–94.

    Article  Google Scholar 

  33. Khan MJ, Hong MJ, Hong KS. Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface. Front Hum Neurosci. 2014; doi:10.3389/fnhum.2014.00244.

    Google Scholar 

  34. Strait M, Scheutz M. What we can and cannot (yet) do with functional near infrared spectroscopy. Front Neurosci. 2014; doi:10.3389/fnins.2014.00117.

    Google Scholar 

  35. Hwang HJ, Lim JH, Kim DW, Im CH. Evaluation of various mental task combinations for near-infrared spectroscopy-based brain-computer interfaces. J Biomed Opt. 2014; doi:10.1117/1.JBO.19.7.077005.

    Google Scholar 

  36. Schudlo LC, Chau T. Dynamic topographical pattern classification of multichannel prefrontal NIRS signals: II. Online differentiation of mental arithmetic and rest. J Neural Eng. 2014; doi:10.1088/1741-2560/11/1/016003.

    Google Scholar 

  37. Cui X, Bray S, Reiss AL. Speeded near infrared spectroscopy (NIRS) response detection. PLoS ONE. 2010; doi:10.1371/journal.pone.0015474.

    Google Scholar 

  38. Kaiser V, Bauernfeind G, Kreilinger A, Kaufmann T, Kübler A, Neuper C, Müller-Putz GR. Cortical effects of user training in a motor imagery based brain-computer interface measured by fNIRS and EEG. NeuroImage. 2014; 85(Pt 1):432–44.

    Article  Google Scholar 

  39. Power SD, Kushki A, Chau T. Intersession consistency of single-trial classification of the prefrontal response to mental arithmetic and the no-control state by NIRS. PLoS One. 2012; doi:10.1371/journal.pone.0037791.

    Google Scholar 

  40. Zhang Q, Strangman GE, Ganis G. Adaptive filtering to reduce global interference in non-invasive NIRS measures of brain activation: How well and when does it work? NeuroImage. 2009; 45(3):788–94.

    Article  Google Scholar 

  41. Saager RB, Telleri NL, Berger AJ. Two-detector corrected near infrared spectroscopy (C-NIRS) detects hemodynamic activation responses more robustly than single-detector NIRS. NeuroImage. 2011; 55(4):1679–85.

    Article  Google Scholar 

  42. Scholkmann F, Metz AJ, Wolf M. Measuring tissue hemodynamics and oxygenation by continuous-wave functional near-infrared spectroscopy-how robust are the different calculation methods against movement artifacts? Physiol Meas. 2014; 35(4):717–34.

    Article  Google Scholar 

  43. Virtanen J, Noponen T, Meriläinen P. Comparison of principal and independent component analysis in removing extracerebral interference from near-infrared spectroscopy signals. J Biomed Opt. 2009; doi:10.1117/1.3253323.

    Google Scholar 

  44. Shibata T. Therapeutic seal robot as biofeedback medical device: qualitative and quantitative evaluations of robot therapy in dementia care. Proc IEEE. 2012; 100(8):2527–38.

    Article  Google Scholar 

  45. Honda, ATR and shimadzu jointly develop brain-machine interface technology enabling control of a robot by human thought alone. http://world.honda.com/news/2009/c090331Brain- Machine-Interface-echnology/. Accessed 30-Sep-2014.

  46. Chang PH, Lee SH, Gu GM, Lee SH, Jin SH, Yeo SS, Seo JP, Jang SH. The cortical activation pattern by a rehabilitation robotic hand: a functional NIRS study. Front Hum Neurosci. 2014; doi:10.3389/fnhum.2014.00049

    Google Scholar 

  47. Shimizu S, Inoue H, Nara H, Tsuruga T, Miwakeichi F, Hirai N, Kikuchi S, Watanabe E, Kato S. Basic study for new assistive technology based on brain activity during car driving. J Robot Mechatron. 2014; 26(2):253–60.

    Google Scholar 

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Authors and Affiliations

  1. School of Information and Communication, Gwangju Institute of Science and Technology, Gwangju, 500-712, Republic of Korea

    Phillips V. Zephaniah & Jae Gwan Kim

  2. Department of Medical System Engineering, Gwangju Institute of Science and Technology, Gwangju, 500-712, Republic of Korea

    Jae Gwan Kim

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  1. Phillips V. Zephaniah
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Correspondence to Jae Gwan Kim.

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Zephaniah, P.V., Kim, J.G. Recent functional near infrared spectroscopy based brain computer interface systems: Developments, applications and challenges. Biomed. Eng. Lett. 4, 223–230 (2014). https://doi.org/10.1007/s13534-014-0156-9

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  • DOI: https://doi.org/10.1007/s13534-014-0156-9

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