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Journal of NeuroEngineering and Rehabilitation
Published in: Journal of NeuroEngineering and Rehabilitation 1/2018

Open Access 01-12-2018 | Research

Assessment of user voluntary engagement during neurorehabilitation using functional near-infrared spectroscopy: a preliminary study

Authors: Chang-Hee Han, Han-Jeong Hwang, Jeong-Hwan Lim, Chang-Hwan Im

Published in: Journal of NeuroEngineering and Rehabilitation | Issue 1/2018

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Abstract

Background

Functional near infrared spectroscopy (fNIRS) finds extended applications in a variety of neuroscience fields. We investigated the potential of fNIRS to monitor voluntary engagement of users during neurorehabilitation, especially during combinatory exercise (CE) that simultaneously uses both, passive and active exercises. Although the CE approach can enhance neurorehabilitation outcome, compared to the conventional passive or active exercise strategies, the active engagement of patients in active motor movements during CE is not known.

Methods

We determined hemodynamic responses induced by passive exercise and CE to evaluate the active involvement of users during CEs using fNIRS. In this preliminary study, hemodynamic responses of eight healthy subjects during three different tasks (passive exercise alone, passive exercise with motor imagery, and passive exercise with active motor execution) were recorded. On obtaining statistically significant differences, we classified the hemodynamic responses induced by passive exercise and CEs to determine the identification accuracy of the voluntary engagement of users using fNIRS.

Results

Stronger and broader activation around the sensorimotor cortex was observed during CEs, compared to that during passive exercise. Moreover, pattern classification results revealed more than 80% accuracy.

Conclusions

Our preliminary study demonstrated that fNIRS can be potentially used to assess the engagement of users of the combinatory neurorehabilitation strategy.
Literature
1.
2.
Greenough WT, Larson JR, Withers GS. Effects of unilateral and bilateral training in a reaching task on branching neurons in the rat motor-sensory forelimb cortex. Behav Neural Boil. 1985;44(2):301–14.
3.
Kleim JA, Barbay S, Nudo RJ. Functional reorganization of the rat motor cortex following motor skill learning. J Neurophysiol. 1998;80(6):3321–5.PubMed
4.
Nudo RJ, Milliken GW, Jenkins WM, Merzenich MM. Use-dependent alterations of movement representations in primary motor cortex of adult squirrel monkeys. J Neurosci. 1996;16(2):785–807.PubMedPubMedCentral
5.
Plautz EJ, Milliken GW, Nudo RJ. Effects of repetitive motor training on movement representations in adult squirrel monkeys: role of use versus learning. Neurobiol Learn Mem. 2000;74(1):27–55.PubMed
6.
Liepert J, Miltnerb WHR, Bauderb H, Sommerb M, Dettmersa E, Taubc E, Weillera C. Motor cortex plasticity during constraint-induced movement therapy in stroke patients. Neurosci Lett. 1998;250(1):5–8.PubMed
7.
Liepert J, Graef S, Uhde I, Leidner O, Weiller C. Training-induced changes of motor cortex representations in stroke patients. Acta Neurol Scand. 2000;101(5):321–6.PubMed
8.
Carey JR, Kimberley TJ, Lewis SM, Auerbach EJ, Dorsey L, Rundquist P, Ugurbil K. Analysis of fMRI and finger tracking training in subjects with chronic stroke. Brain. 2002;125(4):773–88.PubMed
9.
Mima T, Sadato N, Yazawa S, Hanakawa T, Fukuyama H, Yonekura Y, Shibasaki H. Brain structures related to active and passive finger movement in man. Brain. 1999;122(10):1989–97.PubMed
10.
Lotze M, Braun C, Birbaumer N, Anders S, Cohen LG. Motor learning elicited by voluntary drive. Brain. 2003;126(4):866–72.PubMed
11.
Gritsenko V, Prochazka A. A functional electrical stimulation-assisted exercise therapy system for hemiplegic hand function. Arch Phys Med Rehab. 2004;85(6):881–5.
12.
Joa KL, Han YH, Mun CW, Son BK, Lee CH, Shin YB, Ko HY, Shin YI. Evaluation of the brain activation induced by functional electrical stimulation and voluntary contraction using functional magnetic resonance imaging. J Neuroeng Rehabil. 2012;9:48.PubMedPubMedCentral
13.
Kang H, Park W, Kang JH, Kwon GH, Kim SP, Kim L. A neural analysis on motor imagery and passive movement using a haptic device. 12th International Conference on Control, Automation and System. 2012:1536–41.
14.
Ehlis AC, Bähne CG, Jacob CP, Herrmann MJ, Fallgatter AJ. Reduced lateral prefrontal activation in adult patients with attention-deficit/hyperactivity disorder (ADHD) during a working memory task: a functional near-infrared spectroscopy (fNIRS) study. J Psychiatr Res. 2008;42(13):1060–7.PubMed
15.
16.
Sitaram R, Zhang H, Guan C, Thulasidas M, Hoshi Y, Ishikawa A, Shimizu K, Birbaumer N. Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface. NeuroImage. 2007;34(4):1416–27.PubMed
17.
Delpy DT, Cope M, van der Zee P, Arridge S, Wray S, Wyatt J. Estimation of optical pathlength through tissue from direct time of flight measurement. Phys Med Biol. 1988;33(12):1433–42.PubMed
18.
Homan RW, Herman J, Purdy P. Cerebral location of international 10-20 system electrode placement. Electroencephalogr Clin Neurophysiol. 1987;66(4):376–82.PubMed
19.
Pfurtscheller G, Bauernfeind G, Wriessnegger SC, Neuper C. Focal frontal (de)oxyhemoglobin responses during simple arithmetic. Int J Psychophysiol. 2010;76(3):186–92.PubMed
20.
Ang KK, Yu J, Guan C. Extracting effective features from high density NIRS-based BCI for assessing numerical cognition. Conference Proceed. 2012;2012:2233–6.
21.
Hwang HJ, Choi H, Kim JY, Chang WD, Kim DW, Kim K, Jo S, Im CH. Towards more intuitive brain-computer interfacing: classification of binary covert intentions using functional near-infrared spectroscopy. J Biomed Opt. 2016;21(9):091303.PubMed
22.
Bauernfeind G, Leeb R, Wriessnegger SC, Pfurtscheller G. Development, set-up and first results for a one-channel near-infrared spectroscopy system. Biomed Tech. 2008;53(1):36–43.
23.
Power SD, Kushki A, Chau T. 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. 2011;8:066004.PubMed
24.
Moghimi S, Kushki A, Power S, Guerguerian AM, Chau T. Automatic detection of a prefrontal cortical response to emotionally rated music using multi-channel near-infrared spectroscopy. J Neural Eng. 2012;9(2):026022.PubMed
25.
Medvedev AV. Does the resting state connectivity have hemispheric asymmetry? A near-infrared spectroscopy study. NeuroImage. 2014;85(1):400–7.PubMed
26.
Imai M, Watanabe H, Yasui K, Kimura Y, Shitara Y, Tsuchida S, Takahashi N, Taga G. Functional connectivity of the cortex of term and preterm infants and infants with Down’s syndrome. NeuroImage. 2014;85(1):272–8.PubMed
27.
Hwang HJ, Lim JH, Lim DW, Im CH. Evaluation of various mental task combinations for near-infrared spectroscopy-based brain-computer interfaces. J Biomed Opt. 2014;19(7):077005.
28.
Lee S, Koh D, Jo A, Lim HY, Jung YJ, Kim CK, Seo Y, Im CH, Kim BM, Suh M. Depth-dependent cerebral hemodynamic responses following direct cortical electrical stimulation (DCES) revealed by in vivo dual-optical imaging techniques. Opt Express. 2012;20(7):6932–43.PubMed
29.
Han CH, Song H, Kang YG, Kim BM, Im CH. Hemodynamic responses in rat brain during transcranial direct current stimulation: a functional near-infrared spectroscopy study. Biomed Opt Express. 2014;5(6):1812–21.PubMedPubMedCentral
30.
Benaron DA, Hintz SR, Villringer A, Boas D, Kleinschmidt A, Frahm J, Hirth C, Obrig H, van Houten JC, Kermit EL, Cheong WF, Stevenson DK. Noninvasive functional imaging of human brain using light. J Cerebr Blood F Met. 2000;20(3):469–77.
31.
Huppert TJ, Hoge RD, Diamond SG, Franceschini MA, Boas DA. A temporal comparison of BOLD, ASL, and NIRS hemodynamic responses to motor stimuli in adult humans. NeuroImage. 2006;29(2):368–82.PubMed
32.
33.
Tanaka H, Katura T. Classification of change detection and change blindness from near-infrared spectroscopy signals. J Biomed Opt. 2011;16(8):087001.PubMed
34.
Fazli S, Mehnert J, Steinbrink J, Curio G, Villringer A, Müller KR, Blankertz B. Enhanced performance by a hybrid NIRS–EEG brain computer interface. NeuroImage. 2012;59(1):519–29.PubMed
35.
36.
Luu S, Chau T. Decoding subjective preference from single-trial near-infrared spectroscopy signals. J Neural Eng. 2009;6(1):016003.PubMed
37.
Lam L, Suen CY. Application of majority voting to pattern recognition: an analysis of its behavior and performance. IEEE T Syst Man Cy A. 1997;27(5):553–68.
38.
Van Erp M, Vuurpijl L, Schomaker L. An overview and comparison of voting methods for pattern recognition. 8th International Workshop on Frontiers in Handwriting Recognition. 2002:195–200.
39.
Luo A, Sullivan TJ. A user-friendly SSVEP-based brain–computer interface using a time-domain classifier. J Neural Eng. 2010;7(2):026010.
40.
İşcan Z, Dokur Z. A novel steady-state visually evoked potential-based brain–computer interface design: character plotter. Biomed Signal Proces. 2014;10:145–52.
41.
Choularton S, Dale R. User responses to speech recognition errors: consistency of behaviour across domains. 10th Australian International Conference on Speech, Science and Technology (SST). 2004:457–62.
42.
Sellers EW, Kubler A, Donchin E. Brain-computer interface research at the University of South Florida Cognitive Psychophysiology Laboratory: the P300 speller. IEEE T Neur Sys Reh. 2006;14(2):221–4.
43.
Kubler A, Mushahwar VK, Hochberg LR, Donoghue JP. BCI meeting 2005-workshop on clinical issues and applications. IEEE T Neur Sys Reh. 2006;14(2):131–4.
44.
Zimmerli L, Jacky M, Lünenburger L, Riener R, Bolliger M. Increasing patient engagement during virtual reality-based motor rehabilitation. Arch Phys Med Rehab. 2013;94(9):1737–46.
45.
Park W, Kwon GH, Kim DH, Kim YH, Kim SP, Kim L. Assessment of cognitive engagement in stroke patients from single-trial EEG during motor rehabilitation. IEEE T Neur Sys Reh. 2014;23(3):351–62.
46.
Sahyoun C, Floyer-Lea A, Johansen-Berg H, Matthews PM. Towards an understanding of gait control: brain activation during the anticipation, preparation and execution of foot movements. NeuroImage. 2004;21(2):568–75.PubMed
47.
Formaggio E, Storti SF, Galazzo IB, Gandolfi M, Geroin C, Smania N, Spezia L, Waldner A, Fiaschi A, Manganotti P. Modulation of event-related desynchronization in robot-assisted hand performance: brain oscillatory changes in active, passive and imagined movements. J Neuroeng Rehabil. 2013;10:24.PubMedPubMedCentral
48.
Osborne NR, Owen AM, Fernández-Espejo D. The dissociation between command following and communication in disorders of consciousness: an fMRI study in healthy subjects. Front Hum Neurosci. 2015;9:493.
49.
Grefkes C, Nowak DA, Eickhoff SB, Dafotakis M, Küst J, Karbe H, Fink GR. Cortical connectivity after subcortical stroke assessed with functional magnetic resonance imaging. Ann Neurol. 2008;63(2):236–46.PubMed
50.
Wu T, Wang L, Hallett M, Li K, Chan P. Neural correlates of bimanual anti-phase and in-phase movements in Parkinson’s disease. Brain. 2010;133(8):2394–409.PubMedPubMedCentral
Metadata
Title
Assessment of user voluntary engagement during neurorehabilitation using functional near-infrared spectroscopy: a preliminary study
Authors
Chang-Hee Han
Han-Jeong Hwang
Jeong-Hwan Lim
Chang-Hwan Im
Publication date
01-12-2018
Publisher
BioMed Central
Published in
Journal of NeuroEngineering and Rehabilitation / Issue 1/2018
Electronic ISSN: 1743-0003
DOI
https://doi.org/10.1186/s12984-018-0365-z

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