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The feasibility of a brain-computer interface functional electrical stimulation system for the restoration of overground walking after paraplegia
1 Department of Neurology, University of California, Los Angeles, CA, USA
2 Department of Biomedical Engineering, University of California, Irvine, CA, USA
3 Department of Physical TherapyUniversity of California, University of California, Orange, CA, USA
4 Department of NeurologyUniversity of California, University of California, Irvine, CA, USA
5 Department of Electrical Engineering and Computer ScienceUniversity of California, University of California, Irvine, CA, USA
2 Department of Biomedical Engineering, University of California, Irvine, CA, USA
3 Department of Physical TherapyUniversity of California, University of California, Orange, CA, USA
4 Department of NeurologyUniversity of California, University of California, Irvine, CA, USA
5 Department of Electrical Engineering and Computer ScienceUniversity of California, University of California, Irvine, CA, USA
Journal of NeuroEngineering and Rehabilitation 2015, 12:80
doi:10.1186/s12984-015-0068-7
The electronic version of this article is the complete one and can be found online at: http://www.jneuroengrehab.com/content/12/1/80
The electronic version of this article is the complete one and can be found online at: http://www.jneuroengrehab.com/content/12/1/80
| Received: | 4 March 2015 |
| Accepted: | 19 August 2015 |
| Published: | 24 September 2015 |
© 2015 King et al.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Abstract
Background
Direct brain control of overground walking in those with paraplegia due to spinal
cord injury (SCI) has not been achieved. Invasive brain-computer interfaces (BCIs)
may provide a permanent solution to this problem by directly linking the brain to
lower extremity prostheses. To justify the pursuit of such invasive systems, the feasibility
of BCI controlled overground walking should first be established in a noninvasive
manner. To accomplish this goal, we developed an electroencephalogram (EEG)-based
BCI to control a functional electrical stimulation (FES) system for overground walking
and assessed its performance in an individual with paraplegia due to SCI.
Methods
An individual with SCI (T6 AIS B) was recruited for the study and was trained to operate
an EEG-based BCI system using an attempted walking/idling control strategy. He also
underwent muscle reconditioning to facilitate standing and overground walking with
a commercial FES system. Subsequently, the BCI and FES systems were integrated and
the participant engaged in several real-time walking tests using the BCI-FES system.
This was done in both a suspended, off-the-ground condition, and an overground walking
condition. BCI states, gyroscope, laser distance meter, and video recording data were
used to assess the BCI performance.
Results
During the course of 19 weeks, the participant performed 30 real-time, BCI-FES controlled
overground walking tests, and demonstrated the ability to purposefully operate the
BCI-FES system by following verbal cues. Based on the comparison between the ground
truth and decoded BCI states, he achieved information transfer rates >3 bit/s and
correlations >0.9. No adverse events directly related to the study were observed.
Conclusion
This proof-of-concept study demonstrates for the first time that restoring brain-controlled
overground walking after paraplegia due to SCI is feasible. Further studies are warranted
to establish the generalizability of these results in a population of individuals
with paraplegia due to SCI. If this noninvasive system is successfully tested in population
studies, the pursuit of permanent, invasive BCI walking prostheses may be justified.
In addition, a simplified version of the current system may be explored as a noninvasive
neurorehabilitative therapy in those with incomplete motor SCI.
Introduction
Mobility after paraplegia due to spinal cord injury (SCI) is primarily achieved by
substituting the lost function with a wheelchair [1]. However, the sedentary lifestyle associated with excessive wheelchair reliance can
lead to medical co-morbidities, such as osteoporosis, heart disease, respiratory illnesses,
and pressure ulcers [2]. These conditions contribute to the bulk of SCI-related medical care cost [2]. Therefore, restoration of walking after SCI remains a clinical need of high priority.
Current approaches to restoring ambulation after SCI include the use of robotic exoskeletons
[3], [4] and functional electrical stimulation (FES) systems [5], [6]. These devices, however, lack intuitive able-body-like supraspinal control, as they
typically rely on manually controlled switches. In addition, these systems cannot
exploit the neuroplasticity of residual or spared pathways between the brain and spinal
motor pools [7]. Hence, novel means of restoring intuitive, brain-controlled ambulation after SCI
are needed. If successful, such novel approaches may drastically reduce SCI-related
medical costs and improve quality of life after paraplegia due to SCI.
Spinal cord stimulation has recently emerged as a promising method to restore voluntary
lower extremity movements to those with SCI [8], [9]. Brain-computer interfaces (BCIs), which enable intuitive and direct brain control
of walking via an external device [10], [11], can be seen as an alternative approach. Surveys indicate that those with paraplegia
due to SCI highly prioritize restoration of walking as a way of improving their quality
of life [12], [13]. In addition, approximately 60 % of survey participants expressed willingness to
undergo implantation of an invasive BCI device to restore ambulation [13]. However, before such a system can be pursued, it is necessary to establish the feasibility
of brain-controlled overground ambulation. In this proof-of-concept study, we report
on a noninvasive BCI-controlled FES system capable of restoring a basic form of overground
walking to an individual with paraplegia due to SCI. The study advances our existing
BCI systems from applications such as walking in a virtual reality environment (VRE)
[14]–[16] and walking with a treadmill-suspended robotic orthosis [10] to overground walking [11]. If successfully tested in a population of individuals with SCI, the proposed BCI-FES
system may lead to the development of a fully implantable BCI system for restoring
ambulation after SCI.
Methods
Participant screening
Ethical approval was obtained from the University of California, Irvine Institutional
Review Board (Irvine, CA, USA). Candidates were recruited from a population of individuals
with chronic T6 – T12 SCI. They underwent several screening procedures to rule out
severe spasticity, contractures, restricted range of motion, lower extremity fractures,
pressure ulcers, severe osteoporosis, orthostatic hypotension, as well as affirm neuromuscular
responsiveness to FES (see Additional file 1 for details). A physically active 26-year-old male with a T6 AIS B SCI, with no motor
function in the lower extremities and no sensation below the injury level except for
minimally preserved bladder fullness sensation, passed all the screening requirements.
He provided informed consent to participate in the study. He also consented to the
publication of the biomedical data and media, including photographs and videos (consent
to publish was also obtained from every person featured in these photographs and videos).
Training procedure
The participant underwent BCI training to learn how to ambulate within a VRE using
attempted walking and idling (i.e. relaxing) as a control strategy. This procedure
also generated an EEG decoding model that was subsequently used in BCI-FES experiments.
In addition, since the supraspinal areas underlying human gait can become suppressed
after chronic SCI, it has been suggested that motor imagery practice may facilitate
their reactivation [17]. Therefore, the purpose of the BCI-VRE training was to also facilitate the reactivation
of the brain areas responsible for gait control. Finally, the participant simultaneously
underwent FES training to recondition his lower extremity muscles in order to be able
to stand and walk overground using a FDA-approved commercial FES system (Parastep
I System, Sigmedics, Fairborn, OH).
BCI training
Similar to our prior studies [10], [15], [16], the participant first underwent a BCI screening procedure to determine if he could
control the BCI in a VRE. Subsequently, he underwent BCI training in order to further
master BCI-VRE control. Each BCI screening and training visit entailed the same procedure
that began with a 10-min electroencephalogram (EEG) recording. During this period,
the participant engaged in 30-s-long alternating epochs of attempted walking and idling
while seated in his wheelchair [10], [16]. A detailed description of this procedure is given in Additional file 1.
Based on these data, an EEG decoding model was generated offline using the methods
described in [10], [15], [16]. Briefly, the EEG epochs were segmented into 4-s-long trials of “Idle” and “Walk”
class, transformed into the frequency domain, and their power spectral densities (PSDs)
were integrated from 6 to 40 Hz in 2-Hz bins. These spatio-spectral data were then
subjected to dimensionality reduction using classwise principal component analysis
(CPCA) [18], [19], and discriminating features were extracted using approximate information discriminant
analysis (AIDA) [20]. Note that this feature extraction method is rooted in information theory [21] and has been extensively tested in our prior BCI studies [10], [15], [16], [22], [23]. More formally, one-dimensional (1D) features
f∈R were extracted by:
where
d∈RB×C is a single trial of spatio-spectral data (B–number of frequency bins, C–number of electrodes),
Φ:RB×C→Rm is a mapping from the data space to an m-dimensional CPCA-subspace, and
T:Rm→R is an AIDA transformation matrix.
A Bayesian classifier was then designed as follows:
where
P(S1|f⋆) and
P(S2|f⋆) are the posterior probabilities of idling and walking classes, respectively, given
the observed feature, f⋆
. They were found using the Bayes rule
P(Si|f⋆)=p(f⋆|Si)P(Si)/p(f⋆) , i=1,2, where
p(f⋆|Si) is a conditional probability density function (PDF) evaluated at f⋆
,
P(Si) is the prior probability of the class,
Si , and p(f⋆
) is the (unconditional) PDF. To simplify calculations, the conditional PDFs were
modeled as Gaussians with equal variances. Note that this rendered the Bayesian classifier
(2) linear [24]. The performance of the classifier was evaluated offline through stratified ten-fold
cross-validation [25].
Each visit continued with online BCI operation, where 0.75-s-long segments of EEG
data were wirelessly acquired in real time every 0.25 s using a sliding window approach.
The PSDs of the EEG channels were then calculated and integrated in 2 Hz-bins for
each of these segments, and used as the input for the EEG decoding model. The posterior
probabilities,
P(S1|f⋆) and
P(S2|f⋆) , were calculated using the Bayes rule (see above), and were averaged over a 1.5–2.0
s window to minimize false alarms and omissions [10], [15], [16]. Before online BCI operation, the BCI-VRE system was calibrated using a short procedure
(see Additional file 1 for details). During each online experiment, the participant performed between one
and five goal-oriented, real-time BCI walking tasks. Specifically, he was instructed
to utilize attempted walking and idling to control the linear ambulation of an avatar
and make sequential stops at ten designated points within the VRE [14]–[16]. The goal of the task (see Fig. 1) was to walk the avatar at a constant speed and complete the course as quickly as
possible, while dwelling at each stop for at least 2 s. The online performances, expressed
as the number of successful stops and course completion time, were compared to the
results of Monte Carlo simulations to ascertain whether control of the BCI system
was purposeful (details in Additional file 1). Note that despite demonstrating purposeful control during the BCI screening process,
the participant continued the BCI-VRE training throughout the study. This provided
the EEG decoding model for subsequent BCI-FES experiments. It also allowed the participant’s
BCI-VRE performance to be tracked over time and the presumed reactivation of the cortical
gait areas to occur.
Fig. 1. Virtual Reality Environment. A screenshot of the VRE. The traffic cones next to the
characters represent designated stops. A full point was given for dwelling at each
designated stop for at least 2 s, for a total stop score of 10 points. A fraction
of a point was given for dwelling between 0.5 and 2 s (proportionate to the dwelling
time) and no point was given for dwelling less than 0.5 s. There was no penalty for
dwelling for more than 2 s, but this increased the course completion time. As a benchmark,
the course could be completed in ∼205 s with a manually controlled joystick [15], [16]
FES training
To better understand the FES training procedures, a brief description of the Parastep
system’s operation is first provided. Namely, the Parastep achieves ambulation by
activating the quadriceps and tibialis anterior muscles. This is accomplished by placing
electrode pairs bilaterally over the femoral (immediately proximal to the knee) and
deep peroneal (immediately distal to the knee) nerves. Simultaneous bilateral activation
of the quadriceps is used to maintain the knee extension necessary for standing, while
a front-wheel walker is used for upper body stabilization. A step is achieved with
the following sequence: 1. the user performs an anterior-lateral weight shifting maneuver;
2. a brief electrical stimulation is delivered unilaterally to the deep peroneal nerve
while the corresponding quadriceps are deactivated, thereby eliciting a triple-flexion
reflex of the leg (i.e. combination of foot dorsiflexion, knee flexion, and hip flexion);
3. the user’s leg swings forward due to the anteriorly shifted center of gravity;
4. the quadriceps are reactivated to maintain a standing position. The Parastep system’s
adjustable parameters are the step duration (controlled manually by the subject via
buttons) and stimulation current for bilateral femoral and deep peroneal nerves. Based
on these five parameters, the system generates pre-programmed stimulation sequences
for walking movements.
The FDA-approved guidelines for the Parastep system require users to recondition their
muscles prior to engaging in FES-mediated walking. This reconditioning also facilitates
improved cardiopulmonary endurance. To this end, the participant performed strength
and endurance training of the quadriceps using the FES device. Once the participant
regained sufficient strength and endurance, and demonstrated the ability to stand
using the FES system, the training sessions progressed to FES-assisted overground
walking. This included learning the coordination of movements such as weight shifting,
front-wheel walker advancement and leg swing, which facilitate FES-mediated walking.
A more detailed description of these procedures is provided in Additional file 1. It should be noted that the FES training was also used to empirically determine
the stimulation parameters. More specifically, the time necessary to perform the weight
shifting, walker advancement, and leg swing determined the step rate. The stimulation
amplitude for each femoral nerve was determined as the minimal amount of current necessary
to achieve a standing posture. Similarly, the stimulation amplitude for each peroneal
nerve was determined by finding the minimal current necessary to elicit an adequate
triple-flexion response and step. Note that these parameters were later used in the
BCI-FES experiments as described below.
The FES training continued until the participant could walk the length of the overground
walking course (3.66 m) without any intervention from the physical therapist. To prevent
falls and provide partial body-weight support, FES walking was performed while the
participant was mounted in a body-weight support system (ZeroG, Aretech, Ashburn,
VA).
BCI-FES Experiments
The BCI-FES walking experiments were initiated once the participant completed the
FES training. This was accomplished by first integrating the BCI and FES systems using
a dedicated microcontroller. In addition, the step rate and stimulation amplitudes
(as determined above) were pre-programmed into the microcontroller such that the left
and right steps cycle automatically. A motion sensor system was then developed and
synchronized with the BCI-FES system for the purpose of facilitating the performance
assessment. A more detailed description of these steps is provided in Additional file
1. Finally, the EEG decoding model from the most recent BCI training session was loaded
into the BCI system. The participant then undertook suspended BCI-FES walking tests
followed by overground BCI-FES walking tests.
Suspended walking tests
Prior to overground walking, suspended walking tests were performed to establish whether
the participant could purposefully operate the BCI-FES system. First, the participant
was positioned ∼1 m from a computer screen and suspended using the ZeroG support system
so that his feet were ∼5 cm off the ground (see Fig. 2). This allowed the execution of BCI-FES-mediated walking and standing without having
to maintain postural stability, perform weight shifting, or advance the front-wheel
walker. The participant then followed 30-s-long alternating “Idle” and “Walk” visual
computer cues for a total of 180 s with the goal of controlling the standing and walking
functions of the BCI-FES system in real time. Finally, the participant’s performance
(details below) was assessed using video, BCI state, and motion sensor data.
Fig. 2. Experimental setup. Left: The suspended walking test. In response to “Idle” or “Walk” cues displayed on a
computer screen (not shown) the participant modulates his EEG by idling or attempting
to walk. EEG is sent wirelessly (via Bluetooth communication protocol) to the computer,
which processes the data and wirelessly sends a decision to either “Idle” or “Walk”
to a microcontroller. The microcontroller (placed in the belt-pack) drives the FES
of the femoral and deep peroneal nerves to perform either FES-mediated standing or
walking (in place). Right: The overground walking test. In response to verbal cues, the participant performs
BCI-FES mediated walking and standing to walk along a linear course and stop at three
cones positioned 1.8 m apart. The basic components are: the BCI-FES system, motion
sensor system (two gyroscopes and a laser distance meter), and the ZeroG body weight
support system to prevent falls. The information flow from EEG to FES is identical
to that of the suspended walking test. Note that the participant’s face was scrambled
due to privacy concerns
Overground walking tests
For overground walking tests, the participant utilized the system to walk along a
3.66-m-long linear course with three cones positioned 1.83 m apart (Fig. 1). He was instructed to walk and stand at each cone for 10–20 s via verbal cues given
by the experimenter. Subsequently, he used an attempted walking strategy to initiate
BCI-FES-mediated walking to progress to the next cone. Note that the duration of standing
at each cone was randomized to minimize anticipation by the participant. Also note
that the ZeroG system was used during these tests to provide partial body-weight support
and prevent falls. Overground walking tests were repeated as tolerated by the participant.
Video, BCI state, and motion sensor data were recorded to assess the performance during
this task.
Performance assessment
The subject’s performances in the suspended and overground walking tests were derived
based on the video, BCI state, and gyroscope data. Specifically, they were quantified
by calculating the cross-correlation and information transfer rate (ITR) between the
externally supplied cues and BCI-FES-mediated responses. In the suspended walking
tests, the timings of the visual cues were obtained from the BCI computer. In the
overground walking tests, the timings of the auditory cues were extracted from the
video recordings. In both types of tests, the epochs of BCI-FES mediated responses
were extracted from the gyroscope data. Similar to above, purposeful BCI-FES control
was ascertained by comparing these cross-correlations to those achieved by Monte Carlo
simulations (details in Additional file 1). In addition, the instances of false alarms and omissions were recorded, where a
false alarm was defined as the presence of BCI-FES-mediated walking within any intended
idling epoch, while an omission was defined as the absence of BCI-FES-mediated walking
within any intended walking epoch. Finally, in the overground walking tests, the laser
distance meter was used to confirm that the subject ambulated along the course and
stopped at the cones.
Results
Training
The timeline of the study procedures, including the BCI and FES training, is summarized
in Fig. 3. Note that while the participant obtained perfect BCI-VRE control (no omissions or
false alarms) after only 11 h of BCI training, the BCI training continued until the
end of the study in order to verify that the participant could maintain a high-level
of BCI control. In addition, the participant completed the FES training after only
19 FES training sessions, or ∼22.5 h of physical therapy, which is shorter than the
Parastep manufacturer’s nominal recommendation of 32 one-hour sessions.
Fig. 3. Timeline. Experimental time line of the study
BCI training
The performances achieved during the BCI training procedures are shown in Fig. 4. Note that the Bayesian classifier (2) achieved an offline classification accuracy significantly above the chance level
(50 %) on the second visit, and a near-perfect classification accuracy by the 15
th
visit. This translated into a near-perfect level of control during the goal-oriented
real-time BCI walking task within the VRE (Additional file 2), which is evident by the decrease in mean course completion time and increase in
successful stop score. The EEG decoding models resulted in spatio-spectral features
that converged to similar frequencies and brain areas across visits (see Additional
file 1). A sample of these features is depicted in Fig. 5, where areas under electrodes CP3, CPz, and CP4 were deemed by the decoding model
as important for classification of attempted walking and idling. Note that these areas
approximately correspond to the motor and somatosensory cortices. Spectral analysis
confirmed the physiological basis of these features, as event-related synchronization
(ERS) was observed at CP3 and CP4 in the low- β band (13 – 16 Hz), and event-related desynchronization (ERD) was observed at CPz
in the high- β band (23 – 28 Hz).
Fig. 4. BCI training performances over time. BCI training performances over time. Top: Offline performances (%) of the Bayesian classifier (2), as determined through the
cross-validation procedure described in the BCI training section. The bar plots represent
± standard deviation (std). Bottom: Real-time, online BCI-VRE performances expressed as the course completion time (left) and successful stop score (right), determined as explained in Fig. 1. The bar plots represent ± std, and data points with no bars indicate that only one
VRE session was performed on that day. Note that the participant performed less VRE
sessions as the study progressed to make time for more BCI-FES walking sessions
Fig. 5. EEG feature extraction maps. Top: Feature extraction maps obtained by a combination of CPCA and AIDA for classification
of attempted walking versus idling. The spatial distribution of features is shown
for the frequency bands centered at 15 Hz and 25 Hz, where the features with values
close to ±1 are more important for classification. The maps were generated from data
collected during the last visit. Bottom: Log power spectral density (PSD) during idling (blue) and walking (green) at electrodes
CP3, CPz, and CP4, where shaded regions represent error bars. Underneath the PSD plots
are the corresponding signal-to-noise ratio (SNR) plots with significant SNRs (p < 0.01) represented by red lines. Note the event-related synchronization (ERS) in
the 13–16 Hz range (at CP3 and CP4) and event-related desynchronization (ERD) in the
23–28 Hz range (at CPz)
FES training
The participant typically performed one or two FES training sessions per week. The
progression of his FES training is described in detail in Table 1 below. After the visit 19, he demonstrated proper overground walking using the Parastep
system. During this training period, it was empirically determined that the participant
required 4 s to perform each FES-mediated step. This step rate was programmed into
the microcontroller, as explained in the BCI-FES Experiments subsection above. It
was also determined that for the suspended walking test, the participant required
a nominal stimulation of 120 mA at the femoral nerve, and 50 mA at the deep peroneal
nerves. These values were somewhat higher for the overground walking tests, namely,
130 mA for the femoral nerve, and 70 mA and 60 mA for the left and right deep peroneal
nerves, respectively. These stimulation parameters were also used for subsequent BCI-FES
tests.
Table 1. FES training activities across visits. The participant required 19 visits (∼22.5 h
of physical therapy) to comfortably walk 3.66 m
During FES training, the participant experienced a sprain of the left ankle, which
was caused by his outside activities. This condition was resolved after one week of
rest and periodic icing. The participant also experienced occasional light headedness
during his initial attempts of FES-mediated standing and walking. However, this was
no longer an issue after the participant progressed to BCI-FES-mediated walking. No
other adverse events were observed.
Suspended walking tests
Once the BCI and FES training were completed, the suspended walking experiments were
performed on visits 20 and 21 (Additional file 3). The performance metrics of these tests, including the cross-correlation and lag,
number and duration of false alarms and omissions, and the ITR, are presented in Table
2. The participant achieved a very high level of control during this task, as evidenced
by cross-correlations as high as 0.957 and ITRs as high as 3.643 bit/s with no false
alarms or omissions. The subject’s performance in both of the suspended walking tests
was purposeful (p < 0.01), according to the criterion outlined in Additional file 1.
Table 2. The subject’s performances in the suspended walking tests
Overground walking tests
Given the promising results above, the participant started the overground walking
tests on visit 20 (immediately after the first suspended walking test), and continued
these tests until the end of the study (visit 30). In total, 30 overground walking
tests were performed over a 19-week period (see Fig. 2). Between one and six overground walking tests were performed on each visit, with
each test having an average duration, written in the format mean (standard deviation),
of 3.234 (0.743) min. Over time, the participant was able to perform more tests per
visit (see Additional file 1). An average cross-correlation between experimenter’s verbal cues and BCI-FES response
(i.e. leg movement recorded by gyroscopes, see Fig. 6 and Additional file 4) was 0.775 (0.164) with a 2.861 (4.229) s lag. Note that ∼60 % body-weight support
was applied throughout these tests. This value was chosen since it approximates the
contribution of the upper body in the total body weight. It was also found to be comfortable
for the participant and adequate to prevent falls via the ZeroG’s fall detection algorithm.
Fig. 6. Representative space-state-time plot. The best overground walking test results (data
from the 2
nd
test on the 28
th
visit). The beginning and end of yellow blocks mark the onset of the “Walk” and “Idle”
verbal cues, respectively, given by the experimenter. Red blocks represent periods
when the BCI system was in the walk state; otherwise, the system is in the idle state.
Green and blue blocks represent leg movements recorded by the gyroscopes. The laser
signal (blue trace) represents the space-time plot, i.e. the participant’s position
within the course as measured by the laser distance meter. Note that there is a delay
between the onset of the “Idle” cue and the BCI idle state. This latency includes
the time required for the participant’s cognitive processing and EEG to change, as
well as the time required for BCI processing. The discrepancy between the onset of
the idle state and gyroscope signals is due the fact that transitions from the walk
to idle state can be decoded at any time during the pre-programmed 4-s step cycle.
For example, if the state transition occurs during an uninterruptible leg swing, the
participant will finish the leg swing despite the BCI system being in the idle state
(e.g. the first green block). If, on the other hand, the state transition occurs after
a leg swing, the leg will be stationary even before the system enters the idle state
(e.g. the second green block). Finally, the discrepancy between the gyroscope signals
and the distance meter is due to the participant only progressing when the front-wheel
walker is advanced, which happens once every 4 s. Hence, all the leg movements prior
to walker advancement will be registered by the gyroscope, however, they will not
contribute to a position change
The participant had an average of 2.333 (2.039) false alarms (Table 3) and no omissions across all overground walking tests and visits. Comparison to the
Monte Carlo simulations also revealed that all 30 overground walking tests were performed
with purposeful control (p < 0.01). Furthermore, he was able to achieve ITRs similar to the suspended walking
tests. In particular, he had an average ITR of 2.298 (0.889) bit/s across all overground
walking tests, with a maximum ITR of 3.676 bit/s achieved during the second overground
walking test on the 28
th
visit (see Fig. 6). Finally, no adverse events were observed during BCI-FES-mediated overground walking.
Table 3. Cross-correlation (ρ) between verbal cues and gyroscope movement, ITR, number of false alarms, and false
alarm rate for the 30 overground walking tests performed. Note that the false alarm
rates were calculated using the total idling duration. No omissions occurred during
any overground walking session
Discussion
This study represents the first demonstration of an individual with paraplegia due
to SCI purposefully operating a noninvasive BCI-FES system for overground walking
in real time. The participant initially operated the system while being completely
suspended, and subsequently translated this skill to an overground walking condition.
He achieved a high level of control and maintained this level of performance during
a 19-week period. These results provide a proof-of-concept for direct brain control
of a lower extremity prosthesis to restore basic overground walking after paraplegia
due to SCI.
The decoding models for real-time BCI control yielded EEG classification features
that were spatially distributed over the motor and somatosensory cortices. A bilaterally-distributed
ERS in the low- β band (13 – 16 Hz) and a centrally-distributed ERD in the high- β band (23 – 28 Hz) were especially prominent. These findings are consistent with prior
studies [26], [27], where foot motor imagery resulted in an ERS primarily over the hand representation
areas, and an ERD over the foot representation area. These phenomena were observed
in both the μ (8 – 12 Hz) and β (13 – 30 Hz) bands, and are thought to represent an activation of foot representation
area with simultaneous inhibition of networks underlying hand movements [26], [27].
The participant achieved and maintained a high level of performance during the BCI-VRE,
suspended walking and overground walking tests. In comparison to the suspended walking
conditions, there was a notable increase in the false alarm rate during overground
walking. This drop in performance could be explained by an increase in EEG noise produced
by movements, such as postural stabilization or weight shifting. Nevertheless, the
false alarm rate decreased toward the end of the study, presumably due to the participant’s
better understanding of the task as well as practice with operating the BCI. Anecdotally,
the participant was also able to carry a light conversation during these experiments
without interfering with the function of the system. This robustness in real-time
control, together with a high-level of performance sustained across months, indicates
that BCI-FES mediated restoration of basic walking function after SCI is feasible.
Future studies will focus on testing the function of this system in a population of
individuals with SCI. If successfully tested in a larger population, this system may
represent a precursor to invasive BCI systems for overground walking. Namely, the
cumbersome nature of the current noninvasive system makes its adoption for restoration
of overground walking unlikely. This limitation can potentially be addressed by a
fully implantable BCI system, which can be envisioned to employ invasively recorded
neural signals, such as electrocorticogram or action potentials, as well as implantable
spinal cord stimulators [8] or FES systems [28]. Such a fully implantable system would eliminate the need to mount and unmount the
equipment, such as an EEG cap, bioamplifier and a computer, thereby making the implantable
system more practical and aesthetically appealing. Using an invasive system may also
be the only viable approach to deliver cortical stimulation for restoring lower extremity
sensation during walking. Nevertheless, the noninvasive system presented here may
become a safe test bed to determine which individuals are good candidates to receive
these invasive neuroprostheses, once they become available. Furthermore, a simplified
future version of the current system may be applied as neurorehabilitative therapy
for those with incomplete SCI, whereby residual connections between the brain and
spinal motor pools may be strengthened through activity-dependent plasticity mechanisms
[29]. In summary, the system reported here represents an important step toward the development
of technologies that can restore or improve walking in individuals with paraplegia
due to SCI.
Competing interests
CEK received salary from HRL Laboratories, LLC. (Malibu, CA). The authors declare
that they have no competing interests.
Authors’ contributions
CEK integrated the BCI-FES system, built the motion sensor system, conducted the experiments,
performed the data analysis, and wrote the manuscript. PTW implemented the BCI software,
assisted with integrating the BCI-FES system, and provided technical support. CMM
assisted with the experiments. CCYC provided physical therapy and assisted with the
experiments. AHD oversaw and conceived the study, recruited patients, assisted with
integrating the BCI-FES system, and assisted with experiments. ZN oversaw and conceived
the study, designed the signal processing algorithm, and assisted with the experiments.
All authors read and approved the final manuscript.
Additional files
Additional file 1. Appendix. A supplementary document with additional details, as indicated throughout the body
of this report. (PDF 8171 kb)
Format: PDF Size: 8MB Download file
This file can be viewed with: Adobe Acrobat Reader
Format: PDF Size: 8MB Download file
This file can be viewed with: Adobe Acrobat Reader
Additional file 2. Virtual reality training. The participant is engaged in using idling and attempted walking to control the linear
walking of an avatar in a virtual reality environment. (MP4 10,240 kb)Format: MP4 Size: 10MB Download file | Watch movie
Additional file 3. Suspended walking test. The participant is suspended in the air using the ZeroG system. In response to idle/walk
cues, the participant utilizes idling/attempted walking to active/de-activate the
FES system. (MP4 6144 kb)Format: MP4 Size: 6MB Download file | Watch movie
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