Stroke is the leading cause of long-term disability worldwide and the number of affected people increases every year. Though promising work has shown some recovery of upper limb function after a stroke, not all patients exhibit improvement, and regrettably, there is no established method to restore upper limb function to normal.
Brain-computer interfaces (BCI) can, in real-time, record and decode some measurable brain neurophysiological signals and translate brain signal features into a format that may prove useful as a neural feedback system for motor learning in stroke survivors. Previous BCI studies, with non- invasive signal recording approaches, have used electroencephalography (EEG) or magnetoencephalography (MEG), hemodynamic signals based on real-time functional magnetic resonance imaging (rtfMRI), and functional near-infrared spectroscopy (fNIRS). But connecting brain signals with limb movement has been challenging.
Department of Veterans Affairs funded researchers have moved further with brain activated limb movement with a new approach. The method comprises analyzing the brain of a non-impaired subject during body movement using rtfMRI in order to target brain areas associated with specific body movements. Then fNIRS is used to monitor the brain of an impaired individual at the targeted brain areas generating an output signal from the fNIRS device corresponding to brain activity in the targeted brain areas. That output signal is used to produce visual cues to the patient corresponding to positive or negative feedback relating to the extent of brain activity in the targeted brain areas.
From there, the fNIRS device triggers an electrical muscle stimulation device targeting muscles corresponding to brain activity in the targeted brain areas. The functional electrical stimulation (FES) device stimulates muscles to produce movement and afferent nerve impulses which further stimulate the brain, thereby forming a closed-loop feedback system [brain-fNIRS-FES device-afferent nerves] for neural training.
Functional near-infrared spectroscopy enables a low-cost, non-invasive approach for safely measuring brain activity using near-infrared light. Patterns of fNIRS signals during motor execution and imagery are decoded and interpreted, thus making them useful to provide neural feedback for the purpose of motor learning. Minimal movement artifacts help in the precise acquisition of fNIRS signals during motor tasks. Portable versions of the system can be used for a variety of neuro-rehabilitation protocols.
- System can be applied to assistive devices, prosthetics, exoskeletons, and robotic devices for applications in the field of rehabilitation
- Use of fNIRS allows for ease of setup and lack of interference from movement artifacts in comparison to EEG
- fNIRS provides greater speed, portability and affordability compared to fMRI
- Businesses can further develop and commercialize this technology by licensing international patent application WO2017112679 from the VA
- License fees paid to the VA are negotiable
- TechLink navigates businesses through VA invention licensing at no charge
- VA ID: 2015-211