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. 2018 Mar 23;15(1):27.
doi: 10.1186/s12984-018-0365-z.

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

Chang-Hee Han  1 , Han-Jeong Hwang  2 , Jeong-Hwan Lim  1 , Chang-Hwan Im  3
Affiliations

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

Chang-Hee Han et al. J Neuroeng Rehabil. .

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.

Keywords: Functional near-infrared spectroscopy (fNIRS) - motor rehabilitation - neurorehabilitation - combined exercise - pattern classification - hemodynamic response.

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Conflict of interest statement

The authors declare no competing financial interests.

Figures

Fig. 1
Fig. 1
The configuration of optical probes. Red diamonds and blue circles illustrate position of source and detector of fNIRS system, and gray squares indicate position of channels. Distance between a source and detector was 3 cm
Fig. 2
Fig. 2
A schematic diagram of the experimental paradigm. At the beginning of the experiment, instructions for one of the three types of tasks, that is either, PME, PME + MI, or PME + AME appeared on the LCD monitor. One trial consisted of a randomized inter-task rest period ranging from 10 to 15 s and a task period of 10 s. A short beep sound followed before task and rest period
Fig. 3
Fig. 3
Illustration of the in-house hardware system developed by the authors for this study
Fig. 4
Fig. 4
Topographic map of concentration change of oxy, deoxy-, and total-Hb for each task, such as passive exercise (PME), combinatory exercises (PME + MI or PME + AME). Blue points indicate the positions of channels
Fig. 5
Fig. 5
Individual classification accuracy of single-trial pattern classification. Bar with red dotted border shows average classification accuracy. PME vs PME + MI and PME vs PME + AME indicate classification PME versus PME + MI and PME versus PME + AME, respectively. The characters in each bar show best feature set used for each individual
Fig. 6
Fig. 6
Classification accuracies with respect to the number of trials used for the classification. A black dotted vertical line indicates a saturation point. Red triangle and blue rectangle indicate average accuracies of single-trial pattern classification, respectively
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