fNIRS-based Neurorobotic Interface for gait rehabilitation
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RESEARCH
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fNIRS-based Neurorobotic Interface for gait rehabilitation Rayyan Azam Khan1, Noman Naseer1* , Nauman Khalid Qureshi2, Farzan Majeed Noori3, Hammad Nazeer1 and Muhammad Umer Khan1
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. Keywords: Functional near-infrared spectroscopy, Brain-computer interface, Primary motor cortex, Hemodynamic response filter, Linear discriminant analysis, Support vector machine, Computed torque controller
Background Neurological disability due specifically to stroke or spinal cord injury can profoundly affect the social life of paralyzed patients [1–3]. The resultant gait impairment is a large contributor to ambulatory dysfunction [4]. In order to regain complete functional independence, physical rehabilitation remains the mainstay option, owing to the significant expense of health care and the redundancy of therapy sessions. Such devices are developed as alternatives to traditional, expensive and time-consuming exercises * Correspondence: [email protected] 1 Department of Mechatronics Engineering, Air University, Islamabad, Pakistan Full list of author information is available at the end of the article
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