  Objective: Spinal cord injury (SCI) often leaves affected individuals unable
to ambulate. Electroencephalogramme (EEG) based brain-computer interface (BCI)
controlled lower extremity prostheses may restore intuitive and able-body-like
ambulation after SCI. To test its feasibility, the authors developed and tested
a novel EEG-based, data-driven BCI system for intuitive and self-paced control
of the ambulation of an avatar within a virtual reality environment (VRE).
  Approach: Eight able-bodied subjects and one with SCI underwent the following
10-min training session: subjects alternated between idling and walking
kinaesthetic motor imageries (KMI) while their EEG were recorded and analysed
to generate subject-specific decoding models. Subjects then performed a
goal-oriented online task, repeated over 5 sessions, in which they utilised the
KMI to control the linear ambulation of an avatar and make 10 sequential stops
at designated points within the VRE.
  Main results: The average offline training performance across subjects was
77.2 +/- 9.5%, ranging from 64.3% (p = 0.00176) to 94.5% (p = 6.26*10^-23),
with chance performance being 50%. The average online performance was 8.4 +/-
1.0 (out of 10) successful stops and 303 +/- 53 sec completion time (perfect =
211 sec). All subjects achieved performances significantly different than those
of random walk (p < 0.05) in 44 of the 45 online sessions.
  Significance: By using a data-driven machine learning approach to decode
users' KMI, this BCIVRE system enabled intuitive and purposeful self-paced
control of ambulation after only a 10-minute training. The ability to achieve
such BCI control with minimal training indicates that the implementation of
future BCI-lower extremity prosthesis systems may be feasible.
