Abstract

This abstract presents a novel brain-computer interface (BCI) framework to control a prosthetic leg, for the rehabilitation of patients suffering from locomotive disorders, using functional near-infrared spectroscopy (fNIRS). fNIRS signals corresponding to walking intention and rest are used to initiate and stop the gait cycle and a nonlinear proportional derivative computed torque controller (PD-CTC) with gravity compensation is used to control torques of hip and knee joints for minimization of position error. The brain signals of walking intention and rest tasks are acquired from primary motor cortex in the left hemisphere for nine subjects. After acquiring brain signals, in order to remove motion artifacts and physiological noises, the performance of six different filters i.e. Kalman, Wiener, Gaussian, hemodynamic response filter (hrf), Band-pass and finite impulse response, is evaluated. Afterwards, six different features are extracted from oxygenated hemoglobin signals and their different combinations were used for classification. The classification performance of five different classifiers i.e. artificial neural network, quadratic discriminant analysis, linear discriminant analysis (LDA), Naïve Bayes and support vector machine (SVM) is used. The classification accuracies obtained from SVM using hrf filter are significantly higher (p < 0.005) than the other combinations of classifier and filters. These accuracies are 77.5%, 72.5%, 68.3%, 74.2%, 73.3%, 80.8%, 65%, 76.7%, and 86.7% for all nine subjects. The control commands generated using classifier initiate and stop the gait cycle of the prosthetic leg whose knee and hip torques are controlled using PD-CTC to minimize position error. The proposed scheme can be effectively used for neurofeedback training and rehabilitation of lower limb amputation of paralyzed patients.