Abstract
Locomotor malfunction represents a major problem in some neurological disorders like stroke and spinal cord injury. Robot-assisted walking devices have been used during rehabilitation of patients with these ailments for regaining and improving walking ability. Previous studies showed the advantage of brain-computer interface (BCI) based robot-assisted training combined with physical therapy in the rehabilitation of the upper limb after stroke. Therefore, stroke patients with walking disorders might also benefit from using BCI robot-assisted training protocols. In order to develop such BCI, it is necessary to evaluate the feasibility to decode walking intention from cortical patterns during robot-assisted gait training. Spectral patterns in the electroencephalogram (EEG) related to robot-assisted active and passive walking were investigated in 10 healthy volunteers (mean age 32.3±10.8, six female) and in three acute stroke patients (all male, mean age 46.7±16.9, Berg Balance Scale 20±12.8). A logistic regression classifier was used to distinguish walking from baseline in these spectral EEG patterns. Mean classification accuracies of 94.0±5.4% and 93.1±7.9%, respectively, were reached when active and passive walking were compared against baseline. The classification performance between passive and active walking was 83.4±7.4%. A classification accuracy of 89.9±5.7% was achieved in the stroke patients when comparing walking and baseline. Furthermore, in the healthy volunteers modulation of low gamma activity in central midline areas was found to be associated with the gait cycle phases, but not in the stroke patients. Our results demonstrate the feasibility of BCI-based robotic-assisted training devices for gait rehabilitation.
Highlights
Stroke is the main cause of disability in adults [1]
Robot-assisted training devices have been used during rehabilitation of stroke patients for regaining and improving walking ability, offering longer training duration, increasing movement repetitions and reducing the physical load imposed upon the therapist
Before rejection of the EMG components the raw EEG data contains strong power in frequencies in the EMG range, which is spatially clustered around the known muscle sources, i.e. neck, eyes and scalp muscles
Summary
Stroke is the main cause of disability in adults [1]. Many patients present lower limb impairment characterized by abnormal muscle activations. Three months after the stroke, about a quarter of these patients are still bound to the wheelchair [2]. Robot-assisted training devices have been used during rehabilitation of stroke patients for regaining and improving walking ability, offering longer training duration, increasing movement repetitions and reducing the physical load imposed upon the therapist. Robotic training can provide the intensive and task-oriented type of training that has proven effective for promoting motor learning [1,3], which is thought to be useful for motor recovery after stroke [4]. Stroke patients who received electromechanical assisted gait training in combination with physical therapy are more likely to achieve independent walking than patients receiving gait training without these devices [5]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
