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

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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.

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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).

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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

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  1. Keum-Shik Hong
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  3. M. Jawad Khan
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Correspondence to Keum-Shik Hong.

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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

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