Identification of gait imagery based on fNIRS and class-dependent sparse representation. (July 2021)

• Record Type: Journal Article
• Authors: Li, Hongquan; Gong, Anmin; Zhao, Lei; Wang, Fawang; Qian, Qian; Zhou, Jianhua; Fu, Yunfa
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: Functional near infrared spectroscopy (fNIRS) -- Class-dependent sparse representation classification (cdSRC) -- Gait imagery -- Abnormal gait after stroke -- Combined features -- Brain-computer interface (BCI); Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102597
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: This study proposed a relatively new experimental paradigm (normal gait imagery, abnormal gait imagery after stroke and idle state). Compared with other studies, this study is the first time that the class-dependent sparse representation classification (cdSRC) has been introduced to identify three classes of imagery tasks based on fNIRS signals, and it has achieved relatively high classification accuracy under the new experimental paradigm. The study also explored the influence of the HbO signals of mean value, peak value, root mean square and their combined features on classification performance, and found that combined features were more separable than single features. This study is expected to provide a new classification method for fNIRS-BCI system, and also expected to provide an alternative active rehabilitation training method for patients with lower limb motor dysfunction. Abstract: The brain-computer interface (BCI) driven by gait imagery based on functional near-infrared spectroscopy (fNIRS) has potential applications in rehabilitation training for lower limb motor dysfunction, but its performance needs to be improved. The effectiveness of class-dependent sparse representation classification (cdSRC) for identifying gait imagery was explored in the study. First, fNIRS signals were collected from 15 subjects during gait imagery (normal gait imagery and abnormal gait imagery after stroke) and idle state. Mean value, peak value and root mean square of oxyhemoglobin (HbO) and their combinations were calculated as features for classification. Class-dependent K -nearest neighbor (cdKNN) and class-dependent orthogonal matching pursuit (cdOMP) were used to solve HbO features coded by sparse representation and classify them, and the classification results were compared with those obtained by support vector machine (SVM) and KNN. The experimental results showed that the average classification accuracy of three tasks by cdSRC using the combination features of mean value, peak value and root mean square was 87.39 ± 2.59%, which was significantly higher than those achieved by SVM and KNN (78.67 ± 3.96% and 79.78 ± 4.77%, respectively). We discovered that cdSRC combined with fNIRS could effectively identify gait imagery and also proved that the combined features of HbO had better separability than a single feature for gait imagery. Recognizing gait imagery based on fNIRS can be applied to BCI to provide new control commands. This type of BCI can provide active rehabilitation training methods for the disabled, such as providing control commands for mechanical prostheses so that the disabled can perform active rehabilitation training to restore some of their motor functions. In addition, to our knowledge, this study is the first to introduce cdSRC to identify gait imagery (three classes) based on fNIRS.