fNIRS-based Neurorobotic Interface for gait rehabilitation

Abstract

BackgroundIn 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.MethodsfNIRS 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.ResultsThe 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.ConclusionThe 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.

Quantitative Estimation of Differentiated Mental Fatigue between Self-Rising Transfer and Multiple Welfare Robots-Assisted Rising Transfer

“…Therefore, in this paper, we investigated experiments between the two tasks with different difficulty levels. [31,32]. The signal mean, signal slope, signal variance, signal peak, signal kurtosis, and signal skewness were extracted as classification features.…”

“…To acquire brain signals resulting from mental arithmetic tasks, 16 detectors are positioned over the prefrontal cortex, in which each channel corresponds to a specific area of the prefrontal lobe. Multiple notch filters are applied to reduce physiological noises [31,32]. In the measurement process, the signal acquisition equipment is worn on the head, and the signal is transmitted to the PC through Wi-Fi.…”

“…∆c HbR (t) and ∆c HbO (t) signals were obtained by Equation (1). Based on the notch filter with band-reject ranges in Figure 3, 0.1-0.3 Hz signals were acquired and analyzed [31,32]. The signal mean, signal slope, signal variance, signal peak, signal kurtosis, and signal skewness were extracted as classification features.…”

Evolving Toward Subject-Specific Gait Rehabilitation Through Single-Joint Resistive Force Interventions

“…Notably, recent works in the community have been directed toward understanding and categorizing the humanrobot joint misalignments and the compensation strategies (Näf et al, 2018). Furthermore, non-linear control methodologies are being developed and tested with lower limb exoskeletons (van der Kooij et al, 2008;Rea et al, 2014) and prostheses (Zhao et al, 2017;Khan et al, 2018) to compensate for the effect of such misalignments. Alternatively, cable-driven systems that are lightweight, flexible, and do not interfere with the natural gait have been used to develop wearable systems for gait rehabilitation.…”

Wearable Hip-assist Robot Modulates Cortical Activation during Gait in Stroke Patients: A Functional Near-Infrared Spectroscopy Study

“…Compared to other neuroimaging devices, its advantages such as less sensitive to motion artifacts, cheap, portable, safe, and silent, [16] make it the choice for comprehensive and promising results in examination of stroke patients during rehabilitation [13,[17][18][19][20].…”