Brain controlled robotic exoskeleton for neurorehabilitation

Abstract

Robots have been used for decades to enhance productivity, reliability and accuracy for repetitive tasks. More recently robot capabilities have been exploited in medical rehabilitation applications for this same reason. While robots can provide consistent physical therapy there is limited evidence that robot assisted physical therapy has any improved outcomes over human administered therapy. Patient participation is the most important factor for rehabilitating the neural system after injury or stroke and so this research develops a new method for re-connecting the brain to the limbs of a patient. Steady state visual evoked potential (SSVEP) signals are read and decoded to extract the user's intent, and then used to control a robot exoskeleton to move the patient's limbs for therapy. This artificial reconnection of the brain to the limbs allows therapy in a natural way and provides positive reinforcement for learning and so it is believed it will result in improved outcomes. Two different training protocols are proposed and tested to allow real-time brain control of a lower limb rehabilitation device. Results with healthy patients are extremely good with accuracy to within a knee angle of 1° at 100% reliability after simple training. This gives much promise to future development of brain controlled rehabilitation devices.

Review of control strategies for lower-limb exoskeletons to assist gait

“…The user's brain activity is measured using electrodes, amplified and analyzed to determine the mode of operation [37][38][39]. Among the different brain signal recording methods, currently electroencephalography (EEG) is predominantly used since it is non-invasive and therefore 3 Block diagram of the proposed classification of the control strategies subparts.…”

Towards Effective Non-Invasive Brain-Computer Interfaces Dedicated to Gait Rehabilitation Systems

“…When considering studies in the context of lower-limb rehabilitation, some other papers are available. Using an SSVEP paradigm, it was shown that a lower limb exoskeleton could be controlled by both a step by step approach and in a continuous way [ 174 ]. In [ 175 ], a walk rehabilitation system that recognizes five types of intention is proposed and an 80% accuracy is claimed.…”

Prototype of an Ankle Neurorehabilitation System with Heuristic BCI Using Simplified Fuzzy Reasoning

“…Among the promising techniques is a brain-computer interface (BCI) by which electroencephalogram (EEG) signals (i.e., surface electrical brain activity measured on multiple electrodes placed on the scalp) are typically used to operate the rehabilitation device. Such a device provides visual stimulation that evokes brain activity of event-related potentials (ERP) [5,6] or steady-state visually evoked potentials (SSVEP), which correspond to the temporal frequency of the stimulus [7,8].…”

“…For actual rehabilitation purposes, the time allocated to feature extraction should be short, possibly with completion in hours. Existing learning algorithms [5][6][7][8] usually take several weeks to months for machine learning calculations to extract characteristic EEG patterns from a specific frequency range.…”