fNIRS-based Neurorobotic Interface for gait rehabilitation

• Main Authors: Rayyan Azam Khan, Noman Naseer, Nauman Khalid Qureshi, Farzan Majeed Noori, Hammad Nazeer, Muhammad Umer Khan
• Format: Article
• Language: English
• Published: BMC 2018-02-01
• Series: Journal of NeuroEngineering and Rehabilitation
• Subjects: Functional near-infrared spectroscopy; Brain-computer interface; Primary motor cortex; Hemodynamic response filter; Linear discriminant analysis; Support vector machine
• Online Access: http://link.springer.com/article/10.1186/s12984-018-0346-2

Abstract Background In this paper, a novel functional near-infrared spectroscopy (fNIRS)-based brain-computer interface (BCI) framework for control of prosthetic legs and rehabilitation of patients suffering from locomotive disorders is presented. Methods fNIRS signals are used to initiate and stop the gait cycle, while a nonlinear proportional derivative computed torque controller (PD-CTC) with gravity compensation is used to control the torques of hip and knee joints for minimization of position error. In the present study, the brain signals of walking intention and rest tasks were acquired from the left hemisphere’s primary motor cortex for nine subjects. Thereafter, for removal of motion artifacts and physiological noises, the performances of six different filters (i.e. Kalman, Wiener, Gaussian, hemodynamic response filter (hrf), Band-pass, finite impulse response) were evaluated. Then, six different features were extracted from oxygenated hemoglobin signals, and their different combinations were used for classification. Also, the classification performances of five different classifiers (i.e. k-Nearest Neighbour, quadratic discriminant analysis, linear discriminant analysis (LDA), Naïve Bayes, support vector machine (SVM)) were tested. Results The classification accuracies obtained from SVM using the hrf were significantly higher (p < 0.01) than those of the other classifier/ filter combinations. Those accuracies were 77.5, 72.5, 68.3, 74.2, 73.3, 80.8, 65, 76.7, and 86.7% for the nine subjects, respectively. Conclusion The control commands generated using the classifiers initiated and stopped the gait cycle of the prosthetic leg, the knee and hip torques of which were controlled using the PD-CTC to minimize the position error. The proposed scheme can be effectively used for neurofeedback training and rehabilitation of lower-limb amputees and paralyzed patients.