Abstract
BackgroundMany neurological conditions, such as stroke, spinal cord injury, and traumatic brain injury, can cause chronic gait function impairment due to foot-drop. Current physiotherapy techniques provide only a limited degree of motor function recovery in these individuals, and therefore novel therapies are needed. Brain-computer interface (BCI) is a relatively novel technology with a potential to restore, substitute, or augment lost motor behaviors in patients with neurological injuries. Here, we describe the first successful integration of a noninvasive electroencephalogram (EEG)-based BCI with a noninvasive functional electrical stimulation (FES) system that enables the direct brain control of foot dorsiflexion in able-bodied individuals.MethodsA noninvasive EEG-based BCI system was integrated with a noninvasive FES system for foot dorsiflexion. Subjects underwent computer-cued epochs of repetitive foot dorsiflexion and idling while their EEG signals were recorded and stored for offline analysis. The analysis generated a prediction model that allowed EEG data to be analyzed and classified in real time during online BCI operation. The real-time online performance of the integrated BCI-FES system was tested in a group of five able-bodied subjects who used repetitive foot dorsiflexion to elicit BCI-FES mediated dorsiflexion of the contralateral foot.ResultsFive able-bodied subjects performed 10 alternations of idling and repetitive foot dorsifiexion to trigger BCI-FES mediated dorsifiexion of the contralateral foot. The epochs of BCI-FES mediated foot dorsifiexion were highly correlated with the epochs of voluntary foot dorsifiexion (correlation coefficient ranged between 0.59 and 0.77) with latencies ranging from 1.4 sec to 3.1 sec. In addition, all subjects achieved a 100% BCI-FES response (no omissions), and one subject had a single false alarm.ConclusionsThis study suggests that the integration of a noninvasive BCI with a lower-extremity FES system is feasible. With additional modifications, the proposed BCI-FES system may offer a novel and effective therapy in the neuro-rehabilitation of individuals with lower extremity paralysis due to neurological injuries.
Highlights
Many neurological conditions, such as stroke, spinal cord injury, and traumatic brain injury, can cause chronic gait function impairment due to foot-drop
The EEG data associated with epochs of idling and repetitive foot dorsifiexion were analyzed and classified using the prediction model generated from this analysis
While the performance of the current system was tested in a contralaterally-controlled functional electrical stimulation (FES) paradigm, its practical application in individuals with paralysis due to neurological injury will require utilization of an ipsilaterally-controlled FES paradigm, whereby attempted movement of the paralyzed limb acts as the control strategy for its own Brain-computer interface (BCI)-FES mediated movement
Summary
Many neurological conditions, such as stroke, spinal cord injury, and traumatic brain injury, can cause chronic gait function impairment due to foot-drop. We describe the first successful integration of a noninvasive electroencephalogram (EEG)-based BCI with a noninvasive functional electrical stimulation (FES) system that enables the direct brain control of foot dorsiflexion in able-bodied individuals Many neurological conditions, such as stroke, spinal cord injury (SCI), and traumatic brain injury (TBI), can leave the affected individual with severe or complete paralysis. Brain-computer interface (BCI) is a relatively novel technology with the potential to restore, Generally, BCI control of a limb prosthesis is accomplished by acquiring neurophysiological signals associated with a motor process, analyzing these signals in real time, and subsequently translating them into commands for a limb prosthesis To date, this concept has been successfully applied to the control of robotic arms [15] and functional electrical stimulation (FES) devices of the upper extremities [7,14]. Pfurtscheller’s group [7,14] demonstrated how an individual affected by tetraplegia due to SCI was able to utilize a noninvasive electroencephalogram (EEG)-based BCI to control hand grasping via FES to complete a goal-oriented task of grasping an object and moving it another location
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