Detecting self-paced walking intention based on fNIRS technology for the development of BCI
Autor: | Shaolong Kuang, Yufei Zhu, Lining Sun, Jiacheng Xu, Chunguang Li, Wei Qu |
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Rok vydání: | 2020 |
Předmět: |
Male
medicine.medical_specialty Motor dysfunction Computer science Interface (computing) 0206 medical engineering Biomedical Engineering Walking 02 engineering and technology 030218 nuclear medicine & medical imaging Machine Learning Hemoglobins 03 medical and health sciences 0302 clinical medicine Physical medicine and rehabilitation medicine Humans Elderly people Social significance Self paced Brain–computer interface Spectroscopy Near-Infrared Decision Trees Signal Processing Computer-Assisted Linear discriminant analysis 020601 biomedical engineering Frontal Lobe Computer Science Applications Brain-Computer Interfaces Female False positive rate |
Zdroj: | Medical & Biological Engineering & Computing. 58:933-941 |
ISSN: | 1741-0444 0140-0118 |
Popis: | 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 abstract 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. |
Databáze: | OpenAIRE |
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