Hongquan Li1,2, Anmin Gong3, Lei Zhao2,4, Wei Zhang5, Fawang Wang1,2, Yunfa Fu1,2. 1. Institute of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China. 2. Brain Cognition and Brain-Computer Intelligence Fusion Innovation Group, Kunming University of Science and Technology, Kunming 650500, China. 3. Department of Information Engineering, Engineering University of Armed Police Force, Xian 710086, China. 4. Faculty of Science, Kunming University of Science and Technology, Kunming 650500, China. 5. Kunming Medical University, Kunming 650500, China.
Abstract
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 91.55±3.30%, which was significantly higher than those of SVM, KNN, LDA, and LR (86.37±4.42%, 85.65±5.01%, 86.43±4.41%, and 76.14±5.32%, 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 (94.33±2.60%) 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.
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 91.55±3.30%, which was significantly higher than those of SVM, KNN, LDA, and LR (86.37±4.42%, 85.65±5.01%, 86.43±4.41%, and 76.14±5.32%, 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 (94.33±2.60%) 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.
Authors: Androu Abdalmalak; Daniel Milej; Lawrence C M Yip; Ali R Khan; Mamadou Diop; Adrian M Owen; Keith St Lawrence Journal: Front Neurosci Date: 2020-02-18 Impact factor: 4.677