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Detecting self-paced walking intention based on fNIRS technology for the development of BCI

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Abstract

Since more and more elderly people suffer from lower extremity movement problems, it is of great social significance to assist persons with motor dysfunction to walk independently again and reduce the burden on caregivers. The self-paced walking intention, which could increase the ability of self-control on the start and stop of motion, was studied by applying brain–computer interface (BCI) technology, a novel research field. The cerebral hemoglobin signal, which was obtained from 30 subjects by applying functional near-infrared spectroscopy (fNIRS) technology, was processed to detect self-paced walking intention in this paper. Teager–Kaiser energy was extracted at each sampling point for five sub-bands (0.0095~0.021 Hz, 0.021~0.052 Hz, 0.052~0.145 Hz, 0.145~0.6 Hz, and 0.6~2.0 Hz). Gradient boosting decision tree (GBDT) was then utilized to establish the detecting model in real-time. The proposed method had a good performance to detect the walking intention and passed the pseudo-online test with a true positive rate of 100% (80/80), a false positive rate of 2.91% (4822/165171), and a detection latency of 0.39 ± 1.06 s. GBDT method had an area under the curve value of 0.944 and was 0.125 (p < 0.001) higher than linear discriminant analysis (LDA). The results reflected that it is feasible to decode self-paced walking intention by applying fNIRS technology. This study lays a foundation for applying fNIRS-based BCI technology to control walking assistive devices practically.

Graphical representation of the detecting process for pseudo-online test. The lower figure is a partial enlargement of the upper figure. In the lower figure, the blue line represents the probability of walking predicted by GBDT without smoothing and the orange-red line represents the smoothed probability. The dark-red ellipse shows the effect of the smoothing-threshold method.

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Funding

This research was supported by the grants from National Natural Science Foundation of China (61673286), National Natural Science Foundation of China (U1713218), and Postdoctoral Science Foundation (2017T100397).

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Authors and Affiliations

  1. Key Laboratory of Robotics and System of Jiangsu Province School of Mechanical and Electric Engineering, Soochow University, Suzhou, China

    Chunguang Li, Jiacheng Xu, Yufei Zhu, Shaolong Kuang, Wei Qu & Lining Sun

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  1. Chunguang Li
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  2. Jiacheng Xu
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  3. Yufei Zhu
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  4. Shaolong Kuang
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  5. Wei Qu
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  6. Lining Sun
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Correspondence to Yufei Zhu or Shaolong Kuang.

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The participants knew and signed the walking experiment informed consent before the experiment.

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Li, C., Xu, J., Zhu, Y. et al. Detecting self-paced walking intention based on fNIRS technology for the development of BCI. Med Biol Eng Comput 58, 933–941 (2020). https://doi.org/10.1007/s11517-020-02140-w

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  • DOI: https://doi.org/10.1007/s11517-020-02140-w

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