Objectives. Brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) is expected to provide an optional active rehabilitation training method for patients with walking dysfunction, which will affect their quality of life seriously. Sparse representation classification (SRC) oxyhemoglobin (HbO) concentration was used to decode walking imagery and idle state to construct fNIRS-BCI based on walking imagery. Methods. 15 subjects were recruited and fNIRS signals were collected during walking imagery and idle state. Firstly, band-pass filtering and baseline drift correction for HbO signal were carried out, and then the mean value, peak value, and root mean square (RMS) of HbO and their combinations were extracted as classification features; SRC was used to identify the extracted features and the result of SRC was compared with those of support vector machine (SVM), K-Nearest Neighbor (KNN), linear discriminant analysis (LDA), and logistic regression (LR). Results. The experimental results showed that the average classification accuracy for walking imagery and idle state by SRC using three features combination was 91.553.30%, which was significantly higher than those of SVM, KNN, LDA, and LR (86.374.42%, 85.655.01%, 86.434.41%, and 76.145.32%, respectively), and the classification accuracy of other combined features was higher than that of single feature. Conclusions. The study showed that introducing SRC into fNIRS-BCI can effectively identify walking imagery and idle state. It also showed that different time windows for feature extraction have an impact on the classification results, and the time window of 2–8 s achieved a better classification accuracy (94.332.60%) than other time windows. Significance. The study was expected to provide a new and optional active rehabilitation training method for patients with walking dysfunction. In addition, the experiment was also a rare study based on fNIRS-BCI using SRC to decode walking imagery and idle state.

中文翻译：

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基于fNIRS的稀疏表示对行走图像和空闲状态的解码

目标。基于功能性近红外光谱（fNIRS）的脑机接口（BCI）有望为行走功能障碍患者提供一种可选的主动康复训练方法，严重影响其生活质量。稀疏表示分类（SRC）氧合血红蛋白（HbO）浓度用于解码步行图像和空闲状态，以构建基于步行图像的fNIRS-BCI。方法. 招募了 15 名受试者，并在行走图像和空闲状态期间收集了 fNIRS 信号。首先对HbO信号进行带通滤波和基线漂移校正，然后提取HbO及其组合的均值、峰值和均方根（RMS）作为分类特征；SRC用于识别提取的特征，并将SRC的结果与支持向量机（SVM）、K最近邻（KNN）、线性判别分析（LDA）和逻辑回归（LR）的结果进行比较。结果。实验结果表明，SRC 使用三种特征组合对行走图像和空闲状态的平均分类准确率为 91.55 3.30%，显着高于 SVM、KNN、LDA 和 LR（86.374.42%、85.65 5.01%、86.43 4.41%、76.14 5.32%），其他组合特征的分类准确率高于单一特征。结论。研究表明，将 SRC 引入 fNIRS-BCI 可以有效识别行走图像和空闲状态。还表明不同的特征提取时间窗对分类结果有影响，2-8 s的时间窗比其他时间窗取得了更好的分类准确率（94.33 2.60%）。意义. 该研究有望为行走功能障碍患者提供一种新的、可选的主动康复训练方法。此外，该实验也是一项基于 fNIRS-BCI 使用 SRC 解码行走图像和空闲状态的罕见研究。