Operation of a brain-computer interface walking simulator for individuals with spinal cord injury

Abstract

Background: Spinal cord injury (SCI) can leave the affected individuals with paraparesis or paraplegia, thus rendering them unable to ambulate. Since there are currently no restorative treatments for this population, novel approaches such as brain-controlled prostheses have been sought. Our recent studies show that a brain-computer interface (BCI) can be used to control ambulation within a virtual reality environment (VRE), suggesting that a BCI-controlled lower extremity prosthesis for ambulation may be feasible. However, the operability of our BCI has not yet been tested in a SCI population.

Methods: Five participants with paraplegia or tetraplegia due to SCI underwent a 10-min training session in which they alternated between kinesthetic motor imagery (KMI) of idling and walking while their electroencephalogram (EEG) were recorded. Participants then performed a goal-oriented online task, where they utilized KMI to control the linear ambulation of an avatar while making 10 sequential stops at designated points within the VRE. Multiple online trials were performed in a single day, and this procedure was repeated across 5 experimental days.

Results: Classification accuracy of idling and walking was estimated offline and ranged from 60.5% (p = 0.0176) to 92.3% (p = 1.36×10-20) across participants and days. Offline analysis revealed that the activation of mid-frontal areas mostly in the μ and low β bands was the most consistent feature for differentiating between idling and walking KMI. In the online task, participants achieved an average performance of 7.4±2.3 successful stops in 273±51 sec. These performances were purposeful, i.e. significantly different from the random walk Monte Carlo simulations (p<0.01), and all but one participant achieved purposeful control within the first day of the experiments. Finally, all participants were able to maintain purposeful control throughout the study, and their online performances improved over time.

Conclusions: The results of this study demonstrate that SCI participants can purposefully operate a self-paced BCI walking simulator to complete a goal-oriented ambulation task. The operation of the proposed BCI system requires short training, is intuitive, and robust against participant-to-participant and day-to-day neurophysiological variations. These findings indicate that BCI-controlled lower extremity prostheses for gait rehabilitation or restoration after SCI may be feasible in the future.

Figure 1

Finite State Machine. The BCI system as a binary state machine with walking and idling states represented by circles. The state transitions are represented by arrows, with transitions triggered by the conditions shown next to the arrows. Self-pointing arrows denote that the system remains in the present state.

Figure 2

Avatar within the VRE. The avatar controlled by the BCI within the VRE. The simulator is operated in 3 ^rd person view. The participant uses walking KMI to move the avatar to each NPC and idling KMI to dwell there for at least 2 sec.

Figure 3

Feature Extraction Images of Participant 2. Feature extraction images of Participant 2 for all experimental sessions. Dark colors (red and blue) represent the areas that were responsible for encoding the differences between idling and walking KMI. The EEG power in the 9–13 Hz bins in the mid-frontal (FCz), central (Cz) and central-parietal (CPz) areas were the most salient.

Figure 4

Feature Extraction Images of Participant 5. Feature extraction images of Participant 5 for all experimental sessions. The EEG power in the 9–13 Hz bins in the mid-frontal (Fz), lateral central (C3 and C4) and lateral central-parietal (CP3 and CP4) areas were the most informative for encoding the differences between idling and walking KMI.

Figure 5

Calibration Histograms. Histograms of the posterior probability of walking KMI given idling, $P\left(\mathcal{W}\right|f^{\star }\in \mathcal{I})$, and walking KMI given walking, $P\left(\mathcal{W}\right|f^{\star }\in \mathcal{W})$, for Participant 3. Note that $P\left(\mathcal{W}\right|f^{\star }\in \mathcal{I})=1-P(\mathcal{I}|f^{\star }\in \mathcal{I})$. Dashed lines indicate the 25%, 50%, and 75% quantiles.

Figure 6

Best Online Performances. Best online performances (completion times and stop scores) of each participant. Performances are marked by crosses with associated composite scores shown in parenthesis. PDF of the random walk simulations (contour lines) are also shown for Participant 1, but are absent for other participants as the contours lie outside of the alloted 20-min limit. Note that all performances are purposeful (p<0.01).

Figure 7

Online BCI Operation Time-Space Courses. Two representative time courses of the best online sessions for (top) Participant 3 and (bottom) Participant 5, where both subjects were able to achieve a high number of successful stops with a short completion time. False positives (i.e. the avatar walked when the participant intended to stop) are marked by orange segments, and false negatives (i.e. the avatar stopped when the participant intended to walk) are marked by red segments.