  Reliance on wheelchairs after 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 may reduce
the incidence of these conditions, and increase independence and quality of
life. However, no biomedical solution exists that can reverse this lost
neurological function, and hence novel methods are needed. Brain-computer
interface (BCI) controlled lower extremity prosthesis may constitute one such
novel approach.
  One subject with able-body and one with paraplegia due to SCI 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 subjects were 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 averaged 86.3%. The
cross-correlation between instructional cues and BCI-RoGO walking epochs
averaged 0.812 +/- 0.048 (p-value<10^-4). There were on average 0.8 false
alarms per session and no omissions.
  This is the first time a person with parapegia due to SCI regained basic
brain-controlled ambulation, thereby indicating that restoring brain-controlled
ambulation is feasible. Future work will test this system in a population of
individuals with SCI. If successful, this may justify future development of
invasive BCI-controlled lower extremity prostheses. This system may also be
applied to incomplete SCI to improve neurological outcomes beyond those of
standard physiotherapy.
