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

被引:0
作者
Keum-Shik Hong
Usman Ghafoor
M. Jawad Khan
机构
[1] Pusan National University,School of Mechanical Engineering
[2] National University of Science and Technology,School of Mechanical and Manufacturing Engineering
来源
Artificial Life and Robotics | 2020年 / 25卷
关键词
Functional near-infrared spectroscopy; Brain–machine interface; Classification; Stimulation; Neuromodulation;
D O I
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中图分类号
学科分类号
摘要
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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页码:204 / 218
页数:14
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共 679 条
[1]  
Wolpaw JR(2002)Brain–computer interfaces for communication and control Clin Neurophysiol 113 767-791
[2]  
Birbaumer N(2013)A review of EEG-based brain–computer interfaces as access pathways for individuals with severe disabilities Assist Technol 25 99-110
[3]  
McFarland D(2019)Trends in research participant categories and descriptions in abstracts from the international BCI meeting series, 1999 to 2016 Brain Comput Interface 6 13-24
[4]  
Pfurtscheller G(2012)Brain–computer interfaces, a review Sensors 12 1211-1279
[5]  
Vaughan TM(2015)fNIRS-based brain–computer interfaces: a review Front Hum Neurosci 9 3-483
[6]  
Moghimi S(2006)Brain computer-interface research: coming of age Clin Neurophysiol 117 479-636
[7]  
Kushki A(2007)Brain computer interfaces: communication and restoration of movement in paralysis J Physiol 579 621-1328
[8]  
Marie Guerguerian A(2009)Hemodynamic brain–computer interfaces for communication and rehabilitation IEEE Trans Neural Netw Learn Syst 22 1320-560
[9]  
Chau T(2010)Neuroimaging-based approaches in brain–computer interface Trends Biotechnol 28 552-298
[10]  
Eddy BS(2018)Feature extraction and classification methods for hybrid fNIRS-EEG brain–computer interfaces Front Hum Neurosci 12 246-670