Key Laboratory of Robotics and System of Jiangsu Province School of Mechanical and Electric Engineering, Soochow University, Suzhou, China.
Med Biol Eng Comput. 2020 May;58(5):933-941. doi: 10.1007/s11517-020-02140-w. Epub 2020 Feb 21.
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 (0.00950.021 Hz, 0.0210.052 Hz, 0.0520.145 Hz, 0.1450.6 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.
由于越来越多的老年人下肢运动出现问题，帮助运动功能障碍者重新独立行走，减轻照顾者的负担具有重要的社会意义。应用脑-机接口（BCI）技术来研究自我调节步行意图，这是一个新的研究领域，自我调节步行意图可以提高运动开始和停止的自我控制能力。本文应用功能近红外光谱（fNIRS）技术，从 30 名被试者中获取脑血红蛋白信号，对自我调节步行意图进行检测。在五个子频带（0.00950.021 Hz、0.0210.052 Hz、0.0520.145 Hz、0.1450.6 Hz 和 0.6~2.0 Hz）中，每个采样点提取 Teager-Kaiser 能量。然后，利用梯度提升决策树（GBDT）建立实时检测模型。该方法对检测步行意图具有良好的性能，通过真阳性率为 100%（80/80）、假阳性率为 2.91%（4822/165171）、检测潜伏期为 0.39±1.06 s 的伪在线测试。GBDT 方法的曲线下面积值为 0.944，比线性判别分析（LDA）高 0.125（p<0.001）。结果表明，应用 fNIRS 技术解码自我调节步行意图是可行的。该研究为应用基于 fNIRS 的 BCI 技术实际控制步行辅助设备奠定了基础。
