Research on fNIRS Recognition Method of Upper Limb Movement Intention

• Published in: Electronics (Basel) Vol. 10; no. 11; p. 1239
• Main Authors: Li, Chunguang, Xu, Yongliang, He, Liujin, Zhu, Yue, Kuang, Shaolong, Sun, Lining
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2021
• Subjects: Accuracy; Blood; Brain research; Classification; Classifiers; Corrosion; Discriminant analysis; Distance measurement; Experiments; Feature selection; Genetic algorithms; Headgear; Hemoglobin; Human-computer interface; Noise; Paradigms; Performance evaluation; Rehabilitation; Robots; Signal processing; Support vector machines; Training
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics10111239

This paper aims at realizing upper limb rehabilitation training by using an fNIRS-BCI system. This article mainly focuses on the analysis and research of the cerebral blood oxygen signal in the system, and gradually extends the analysis and recognition method of the movement intention in the cerebral blood oxygen signal to the actual brain-computer interface system. Fifty subjects completed four upper limb movement paradigms: Lifting-up, putting down, pulling back, and pushing forward. Then, their near-infrared data and movement trigger signals were collected. In terms of the recognition algorithm for detecting the initial intention of upper limb movements, gradient boosting tree (GBDT) and random forest (RF) were selected for classification experiments. Finally, RF classifier with better comprehensive indicators was selected as the final classification algorithm. The best offline recognition rate was 94.4% (151/160). The ReliefF algorithm based on distance measurement and the genetic algorithm proposed in the genetic theory were used to select features. In terms of upper limb motion state recognition algorithms, logistic regression (LR), support vector machine (SVM), naive Bayes (NB), and linear discriminant analysis (LDA) were selected for experiments. Kappa coefficient was used as the classification index to evaluate the performance of the classifier. Finally, SVM classification got the best performance, and the four-class recognition accuracy rate was 84.4%. The results show that RF and SVM can achieve high recognition accuracy in motion intentions and the upper limb rehabilitation system designed in this paper has great application significance.