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.

中文翻译：

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基于fNIRS技术的自定步伐步行意图检测，以开发BCI。

由于越来越多的老年人患有下肢运动问题，因此，协助运动功能障碍的人再次独立行走并减轻护理人员的负担具有重要的社会意义。通过应用脑计算机接口（BCI）技术，这是一个新兴的研究领域，研究了可以提高运动开始和停止自控能力的自定步伐步行意图。本文应用功能近红外光谱（fNIRS）技术从30名受试者中获得了脑血红蛋白信号，并对其进行了处理，以检测自定步态的步行意图。在每个采样点的五个子带（0.0095〜0.021 Hz，0.021〜0.052 Hz，0.052〜0.145 Hz，0.145〜0.6 Hz和0.6〜2.0 Hz）中提取Teager-Kaiser能量。然后利用梯度提升决策树（GBDT）实时建立检测模型。提出的方法具有良好的步行意图检测性能，通过伪在线测试，真实阳性率为100％（80/80），错误阳性率为2.91％（4822/165171），检测潜伏期为0.39±1.06 s。GBDT方法的曲线下面积为0.944，比线性判别分析（LDA）高0.125（p <0.001）。结果表明，应用fNIRS技术对自定步态的步行意图进行解码是可行的。该研究为将基于fNIRS的BCI技术应用于实际控制步行辅助设备奠定了基础。图形摘要伪在线测试的检测过程的图形表示。下图是上图的局部放大。在下图中，蓝线表示不进行平滑处理而由GBDT预测的步行概率，而橙红线表示经过平滑处理的概率。深红色椭圆显示了平滑阈值方法的效果。