Objective: Most BCI (brain-computer interface) studies have focused on detecting motion intention from a resting state. However, the dynamic regulation of two motion states, which usually happens in real life, is rarely studied. Besides, popular within-subject methods also require an extensive and time-consuming learning stage when testing on a new subject. This paper proposed a method to discriminate dynamic gait- adjustment intention with strong adaptability for different subjects. Methods: Cerebral hemoglobin signals obtained from 30 subjects were studied to decode gait-adjustment intention. Cerebral hemoglobin information was recorded by using fNIRS (functional near infrared spectroscopy) technology. Mathematical morphology filtering was applied to remove zero drift and EWM (Entropy Weight Method) was used to calculate the average hemoglobin values over Regions of Interest (ROIs). The gradient boosting decision tree (GBDT) was utilized to detect the onset of self-regulated intention. A 2-layer-GA-SVM (Genetic Algorithm-Support Vector Machine) model based on stacking algorithm was further proposed to identify the four types of self-regulated intention (speed increase, speed reduction, step increase, and step reduction). Results: It was found that GBDT had a good performance to detect the onset intention with an average AUC (Area Under Curve) of 0.894. The 2-layer-GA-SVM model boosted the average ACC (accuracy) of four types of intention from 70.6% to 84.4% (${p} =0.005$ ) from the single GA-SVM model. Furthermore, the proposed method passed pseudo-online test with the average results as following: AUC = 0.883, TPR (True Positive Rate) = 97.5%, FPR (False Positive Rate) = 0.11%, and LAY (Detection Latency) = -0.52 ± 2.57 seconds for the recognition of gait-adjustment intention; ACC = 80% for the recognition of adjusted gait. Conclusion: The results indicate that it is feasible to decode dynamic gait-adjustment intentions from a motion state for different subjects based on fNIRS technology. It has a potential to realize the practical application of fNIRS-based brain-computer interface technology in controlling walking-assistive devices.

中文翻译：

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检测步行过程中自我调节意图的受试者间 fNIRS-BCI 研究


目的：大多数 BCI（脑机接口）研究都集中在检测静止状态下的运动意图。然而，现实生活中常见的两种运动状态的动态调节却很少被研究。此外，流行的科目内方法在测试新科目时也需要广泛且耗时的学习阶段。本文提出了一种对不同被试适应性强的动态步态调整意图判别方法。方法：研究从 30 名受试者获得的脑血红蛋白信号来解码步态调整意图。使用fNIRS（功能近红外光谱）技术记录脑血红蛋白信息。应用数学形态学过滤来消除零漂移，并使用 EWM（熵权法）来计算感兴趣区域 (ROI) 上的平均血红蛋白值。利用梯度提升决策树（GBDT）来检测自我调节意图的发生。进一步提出基于堆叠算法的2层GA-SVM（遗传算法-支持向量机）模型来识别四种类型的自我调节意图（加速、减速、加步、减步）。结果：发现GBDT在检测起始意图方面具有良好的性能，平均AUC（曲线下面积）为0.894。 2 层 GA-SVM 模型将四种意图的平均 ACC（准确率）从单一 GA-SVM 模型的 70.6% 提高到 84.4% (${p} =0.005$)。此外，该方法通过了伪在线测试，平均结果如下：AUC = 0.883，TPR（True Positive Rate）= 97.5%，FPR（False Positive Rate）= 0.11%，LAY（Detection Latency）= -0.52 ±2。步态调整意图识别57秒； ACC = 80% 用于识别调整后的步态。结论：结果表明基于fNIRS技术从运动状态解码不同被试的动态步态调整意图是可行的。有望实现基于fNIRS的脑机接口技术在步行辅助设备控制中的实际应用。