Brain-Computer Interface Controlled Robotic Gait Orthosis: A Case Report

Abstract

Reliance on wheelchairs in individuals with spinal cord injury (SCI) leads to many medical co-morbidities. Treatment of these conditions contributes to the majority of SCI health care costs. Restoring able-body-like ambulation after SCI can potentially reduce the incidence of these conditions, as well as increase independence and quality of life. However, no biomedical solution exists that can reverse this loss of neurological function, and hence novel methods are needed. Brain-computer interface (BCI) controlled lower extremity prosthesis may constitute one such novel approach. An able-bodied subject underwent electroencephalogram (EEG) recording while engaged in alternating epochs of idling and walking kinesthetic motor imagery (KMI). These data were analyzed to generate an EEG prediction model for online BCI operation. A commercial robotic gait orthosis (RoGO) system (treadmill suspended), was interfaced with the BCI computer. In an online test, the subject was tasked to ambulate using the BCI-RoGO system when prompted by computerized cues. The performance of this system was assessed with cross-correlation analysis, and omission and false alarm rates. The offline accuracy of the EEG prediction model was 94.8 +/- 0.8. The cross-correlation between instructional cues and the subject's BCI-RoGO walking epochs averaged over 5 online sessions was 0.809 +/- 0.056 (p-value<10^-5). There were on average 0.8 false alarms per session and no omissions. These results provide early evidence that restoring brain-controlled ambulation is feasible. Future work will test this system in individuals with SCI. If successful, this may justify development of BCI-controlled lower extremity prostheses for free overground walking for those with complete motor SCI. This system may also be applied to incomplete motor SCI to improve neurological outcomes beyond those of standard physiotherapy.